Combining horizontal and vertical substructure relationships in scaffold hierarchies for activity prediction

For a systematic exploration of structural relationships between molecular scaffolds, ～24,000 unique scaffolds were extracted from 458 different target sets. Substructure relationships between these scaffolds were systematically determined. The scaffold tree data structure was utilized to study structural relationships between original scaffolds and derivative scaffolds obtained by rule-based decomposition. Leaf-to-root substructure relationships that resulted from rule-based decomposition were compared to leaf-to-leaf relationships between original scaffolds most of which were not part of the scaffold tree hierarchy. Decomposed scaffolds not contained in active target set compounds were prioritized on the basis of hierarchical scaffold patterns and additional substructure relationships. For high-priority virtual scaffolds, activity predictions were carried out, and these scaffolds were often found in external test compounds having the predicted activity. Taken together, our results suggest that leaf-to-root substructure relationships in scaffold trees should best be complemented with additional substructure relationships to determine high-priority virtual scaffolds for activity prediction.     

Identification of Novel Covalent XPO1 Inhibitors Based on a Hybrid Virtual Screening Strategy

Nuclear export protein 1 (XPO1), a member of the nuclear export protein-p (Karyopherin-P) superfamily, regulates the transport of “cargo” proteins. To facilitate this important process, which is essential for cellular homeostasis, XPO1 must first recognize and bind the cargo proteins. To inhibit this process, small molecule inhibitors have been designed that inhibit XPO1 activity through covalent binding. However, the scaffolds for these inhibitors are very limited. While virtual screening may be used to expand the diversity of the XPO1 inhibitor skeleton, enormous computational resources would be required to accomplish this using traditional screening methods. In the present study, we report the development of a hybrid virtual screening workflow and its application in XPO1 covalent inhibitor screening. After screening, several promising XPO1 covalent molecules were obtained. Of these, compound 8 performed well in both tumor cell proliferation assays and a nuclear export inhibition assay. In addition, molecular dynamics simulations were performed to provide information on the mode of interaction of compound 8 with XPO1. This research has identified a promising new scaffold for XPO1 inhibitors, and it demonstrates an effective and resource-saving workflow for identifying new covalent inhibitors.     

Virtual screening of some heterocyclic structures toward novel antibacterial agents

Infectious diseases and their treatment are among the most important issues in global health and economy. Moreover, increasing prevalence of antibiotic-resistant pathogenic bacteria necessitates the considerable need for discovering new drugs. Some heterocyclic structures with dihydropyridine (DHP) scaffold have been reported as antimicrobial agents. Herein, we report a structure-based virtual screening of a pool of 3,5-disubstituted DHPs and a post-analysis of virtual hits through in vitro antibacterial assessment. Four top-ranked DHP structures (6a–d) were found to interact with the relevant target active sites and exhibited superior stereoelectronic features within their enzyme inhibition. Selected compounds were synthesized and assessed for their antibacterial activity via microdilution method. Results of this study represented a significant application of multi-step virtual screening strategy in identifying privileged DHP structures as good starting points for further developments toward more potent antibacterial agents.     

Molecular modeling studies to discover novel mIDH2 inhibitors with high selectivity for the primary and secondary mutants

Mutant isocitrate dehydrogenase 2 (mIDH2) is an emerging target for the treatment of cancer. AG-221 is the first mIDH2 inhibitor approved by the FDA for acute myeloid leukemia treatment, but its acquired resistance has recently been observed, necessitating the development of new inhibitor. In this study, a multi-step virtual screening protocol was employed for the analysis of a large database of compounds to identify potential mIDH2 inhibitors. To this end, we firstly utilized molecular dynamics (MD) simulations and binding free energy calculations to elucidate the key factors affecting ligand binding and drug resistance. Based on these findings, the receptor-ligand interaction-based pharmacophore (IBP) model and hierarchical docking-based virtual screening were sequentially carried out to assess 212,736 compounds from the Specs database. The resulting hits were finally ranked by PAINS filter and ADME prediction and the top compounds were obtained. Among them, six molecules were identified as mIDH2 putative inhibitors with high selectivity by interacting with the capping residue Asp312. Furthermore, subsequent docking and MD experiments demonstrated that compound V2 might have potential inhibitory activity against the AG-221-resistant mutants, thereby making it a promising lead for the development of novel mIDH2 inhibitors.     

Identification of potential Tpx inhibitors against pathogen-host interactions

Yersinia organisms cause many infectious diseases by invading human cells and delivering their virulence factors via the type three secretion system (T3SS). One alternative strategy in the fight against these pathogenic organisms is to interfere with their T3SS. Previous studies demonstrated that thiol peroxidase, Tpx is functional in the assembly of T3SS and its inhibition by salicylidene acylhydrazides prevents the secretion of pathogenic effectors. In this study, the aim was to identify potential inhibitors of Tpx using an integrated approach starting with high throughput virtual screening and ending with molecular dynamics simulations of selected ligands. Virtual screening of ZINC database of 500,000 compounds via ligand-based and structure-based pharmacophore models retrieved 10,000 hits. The structure-based pharmacophore model was validated using high-throughput virtual screening (HTVS). After multistep docking (SP and XP), common scaffolds were used to find common substructures and the ligand binding poses were optimized using induced fit docking. The stability of the protein–ligand complex was examined with molecular dynamics simulations and the binding free energy of the complex was calculated. As a final outcome eight compounds with different chemotypes were proposed as potential inhibitors for Tpx. The eight ligands identified by a detailed virtual screening protocol can serve as leads in future drug design efforts against the destructive actions of pathogenic bacteria.     

A pseudo-ligand approach to virtual screening

A virtual screening method is presented that is grounded on a receptor-derived pharmacophore model termed "virtual ligand" or "pseudo-ligand". The model represents an idealized constellation of potential ligand sites that interact with residues of the binding pocket. For rapid virtual screening of compound libraries the potential pharmacophore points of the virtual ligand are encoded as an alignment-free correlation vector, avoiding spatial alignment of pharmacophore features between the pharmacophore query (i.e., the virtual ligand) and the candidate molecule. The method was successfully applied to retrieving factor Xa inhibitors from a Ugi three-component combinatorial library, and yielded high enrichment of actives in a retrospective search for cyclooxygenase-2 (COX-2) inhibitors. The approach provides a concept for "de-orphanizing" potential drug targets and identifying ligands for hitherto unexplored or allosteric binding pockets.     

Computational fragment-based design of Wee1 kinase inhibitors with tricyclic core scaffolds

Wee1 is cell cycle protein comprising a kinase domain and is a validated cancer target. We have designed molecules with variable tricyclic core scaffolds [6-6-5] system and extended them based on the chemical space available in the active site of Wee1 kinase using de novo drug design. The core scaffolds and linking fragments were extracted from pharmacophore-based virtual screening of ZINC and PubChem databases and Ludi library. These molecules bind the hinge region of kinase active site and form hydrogen bonds as confirmed from molecular docking, molecular dynamics simulations, and MM_PBSA calculations. When compared with reference inhibitors, AZD1775 and PHA-848125, the de novo designed molecules also show good docking scores and stability, retained non-covalent interactions, and high binding free energies contributed from active site residues.

     

Discovery of novel inhibition site centered on 114-bit tryptophan of Thioredoxin reductase 1 through computer-aided drug design

Thioredoxin reductase 1 (TrxR1) is an oxidoreductase playing the important role in the tumor cells. It is a new type of drug therapy target. Most of the existing TrxR1 inhibitors act directly covalently on the active sites. Herein, molecular docking-based virtual screening approach was used to screen inhibitors with new binding site of TrxR1 from the SPECS database. After experimental test, compound 22 was identified as the reversibility inhibitor of TrxR1 U498C mutant (It has similar structure and function to replace the wild-type TrxR1 which is difficult to express) with IC₅₀ value of 15.31 ± 0.57 μM. The molecular docking results showed that the interaction between compound 22 and TrxR1 was centered on inactive site Trp¹¹⁴. Furthermore, phenazine compounds 24–30 with similar structures as 22 were also screened out from our phenazine database. Compounds 24–27 had longer chain structures and better inhibitory activity than compound 22, while compounds 28–30 were the opposite. Compounds 24–27 can be more stably bound in the protein cavity on Trp¹¹⁴ than compounds 28–30. Then we verified amino acids centered on Trp¹¹⁴ can regulate TrxR1 activity by amino acids mutation. Taken together, A new inhibition site are found that can regulate TrxR1 U498C mutant activity by acting on amino acids sequence at inactive sites centered on Trp¹¹⁴ and can provide ideas for the discovery and research of new TrxR1 inhibitors.     

Development of a method to consistently quantify the structural distance between scaffolds and to assess scaffold hopping potential

We introduce a method to determine a structural distance between any pair of molecular scaffolds. The development of this approach was motivated by the need to accurately evaluate scaffold hopping studies in virtual screening and medicinal chemistry and assess the degree of difficulty involved in facilitating a transition from one structure to another. In order to consistently derive structural distances, scaffolds of different composition and topology are subjected to molecular editing procedures that abstract from original scaffolds in a defined manner until compositional and topological equivalence can be established. Pairs of corresponding scaffold representations are transformed into one-dimensional atom sequences that are aligned using approaches adapted from biological sequence comparison. From best scoring atom sequence alignments, interscaffold distances are derived. The algorithm is evaluated at different levels including the analysis of a series of model scaffolds with defined chemical changes, a scaffold library, and scaffolds from reference compounds and hits of successful virtual screening applications. It is demonstrated that chemically intuitive scaffold distances are obtained for pairs of scaffolds with varying composition and topology. Distance threshold values for close and remote structural relationships between scaffolds are also determined. The methodology is made publicly available in order to provide a basis for a consistent assessment of scaffold hopping ability and to aid in the evaluation and comparison of virtual screening methods.     

Druggable Allosteric Sites in β-Propeller Lectins

Carbohydrate-binding proteins (lectins) are auspicious targets in drug discovery to combat antimicrobial resistance; however, their non-carbohydrate drug-like inhibitors are still unavailable. Here, we present a druggable pocket in a β-propeller lectin BambL from Burkholderia ambifaria as a potential target for allosteric inhibitors. This site was identified employing ¹⁹F NMR fragment screening and a computational pocket prediction algorithm SiteMap. The structure–activity relationship study revealed the most promising fragment with a dissociation constant of 0.3±0.1 mM and a ligand efficiency of 0.3 kcal mol^?1 HA^?1 that affected the orthosteric site. This effect was substantiated by site-directed mutagenesis in the orthosteric and secondary pockets. Future drug-discovery campaigns that aim to develop small molecule inhibitors can benefit from allosteric sites in lectins as a new therapeutic approach against antibiotic-resistant pathogens.     

Two-track virtual screening approach to identify both competitive and allosteric inhibitors of human small C-terminal domain phosphatase 1

Despite a wealth of persuasive evidence for the involvement of human small C-terminal domain phosphatase 1 (Scp1) in the impairment of neuronal differentiation and in Huntington’s disease, small-molecule inhibitors of Scp1 have been rarely reported so far. This study aims to the discovery of both competitive and allosteric Scp1 inhibitors through the two-track virtual screening procedure. By virtue of the improvement of the scoring function by implementing a new molecular solvation energy term and by reoptimizing the atomic charges for the active-site Mg²⁺ ion cluster, we have been able to identify three allosteric and five competitive Scp1 inhibitors with low-micromolar inhibitory activity. Consistent with the results of kinetic studies on the inhibitory mechanisms, the allosteric inhibitors appear to be accommodated in the peripheral binding pocket through the hydrophobic interactions with the nonpolar residues whereas the competitive ones bind tightly in the active site with a direct coordination to the central Mg²⁺ ion. Some structural modifications to improve the biochemical potency of the newly identified inhibitors are proposed based on the binding modes estimated with docking simulations.     

An integrated in silico screening strategy for identifying promising disruptors of p53-MDM2 interaction

The p53 protein, also called guardian of the genome, plays a critical role in the cell cycle regulation and apoptosis. This protein is frequently inactivated in several types of human cancer by abnormally high levels of its negative regulator, mouse double minute 2 (MDM2). As a result, restoration of p53 function by inhibiting p53-MDM2 protein–protein interaction has been pursued as a compelling strategy for cancer therapy. To date, a limited number of small-molecules have been reported as effective p53−MDM2 inhibitors. X-ray structures of MDM2 in complex with some ligands are available in Protein Data Bank and herein, these data have been exploited to efficiently identify new p53-MDM2 interaction antagonists through a hierarchical virtual screening strategy. For this purpose, the first step was aimed at compiling a focused library of 686,630 structurally suitable compounds, from PubChem database, similar to two known effective inhibitors, Nutlin-3a and DP222669. These compounds were subjected to the subsequent structure-based approaches (quantum polarized ligand docking and molecular dynamics simulation) to select potential compounds with highest binding affinity for MDM2 protein. Additionally, ligand binding energy, ADMET properties and PAINS analysis were also considered as filtering criteria for selecting the most promising drug-like molecules. On the basis of these analyses, three top-ranked hit molecules, CID_118439641, CID_60452010 and CID_3106907, were found to have acceptable pharmacokinetics properties along with superior in silico inhibitory ability towards the p53-MDM2 interaction compared to known inhibitors. Molecular docking and molecular dynamics results well confirmed the interactions of the final selected compounds with critical residues within p53 binding site on the MDM2 hydrophobic clefts with satisfactory thermodynamics stability. Consequently, the new final scaffolds identified by the presented computational approach could offer a set of guidelines for designing promising anti-cancer agents targeting p53-MDM2 interaction.     

Integrating pharmacophore mapping,virtual screening,density functional theory,molecular simulation towards the discovery of novel apolipoprotein (apoE ε4) inhibitors

AimAn integrated protocol of virtual screening involving molecular docking, pharmacophore probing, and simulations was established to identify small novel molecules targeting crucial residues involved in the variant apoE ε4 to mimic its behavior as apoE2 thereby eliminating the amyloid plaque accumulation and facilitating its clearance.Materials and MethodsAn excellent ligand-based and structure-based approach was made to identify common pharmacophoric features involving structure-based docking with respect to apoE ε4 leading to the development of apoE ε4 inhibitors possessing new scaffolds. An effort was made to design multiple-substituted triazine derivatives series bearing a novel scaffold. A structure-based pharmacophore mapping was developed to explore the binding sites of apoE ε4 which was taken into consideration. Subsequently, virtual screening, ADMET, DFT searches were at work to narrow down the proposed hits to be forwarded as a potential drug likes candidates. Further, the binding patterns of the best-proposed hits were studied and were forwarded for molecular dynamic simulations of 10 ns for its structural optimization.ResultsSelectivity profile for the most promising candidates was studied, revealing significantly C13 and C15 to be the most potent compounds. The proposed hits can be forwarded for further study against apoE ε4 involved in neurological disorder Alzheimer’s.     

Development of Pseudomonas aeruginosa Lectin LecA Inhibitor by using Bivalent Galactosides Supported on Polyproline Peptide Scaffolds

LecA is a galactose‐binding tetrameric lectin from Pseudomonas aeruginosa involved in infection and biofilm formation. The emergent antibiotic resistance of P. aeruginosa has made LecA a promising pharmaceutical target to treat such infections. To develop LecA inhibitors, we exploit the unique helical structure of polyproline peptides to create a scaffold that controls the galactoside positions to fit their binding sites on LecA. With a modular scaffold design, both the galactoside ligands and the inter‐ligand distance can be altered conveniently. We prepared scaffolds with spacings of 9, 18, 27, and 36 Å for ligand conjugation and found that glycopeptides with galactosides ligands three helical turns (27 Å) apart best fit LecA. In addition, we tested different galactose derivatives on the selected scaffold (27 Å) to improve the binding avidity to LecA. The results validate a new multivalent scaffold design and provide useful information for LecA inhibitor development.     

The selectivity and promiscuity of brain-neuroregenerative inhibitors between ROCK1 and ROCK2 isoforms: An integration of SB-QSSR modelling,QM/MM analysis and in vitro kinase assay

The Rho-associated kinases (ROCKs) have long been recognized as an attractive therapeutic target for various neurological diseases; selective inhibition of ROCK1 and ROCK2 isoforms would result in distinct biological effects on neurogenesis, neuroplasticity and neuroregeneration after brain surgery and traumatic brain injury. However, the discovery and design of isoform-selective inhibitors remain a great challenge due to the high conservation and similarity between the kinase domains of ROCK1 and ROCK2. Here, a structure-based quantitative structure–selectivity relationship (SB-QSSR) approach was used to correlate experimentally measured selectivity with the difference in inhibitor binding to the two kinase isoforms. The resulting regression models were examined rigorously through both internal cross-validation and external blind validation; a nonlinear predictor was found to have high fitting stability and strong generalization ability, which was then employed to perform virtual screening against a structurally diverse, drug-like compound library. Consequently, five and seven hits were identified as promising candidates of 1-o-2 and 2-o-1 selective inhibitors, respectively, from which seven purchasable compounds were tested in vitro using a standard kinase assay protocol to determine their inhibitory activity against and selectivity between ROCK1 and ROCK2. The structural basis, energetic property and biological implication underlying inhibitor selectivity and promiscuity were also investigated systematically using a hybrid quantum mechanics/molecular mechanics (QM/MM) scheme.     

Sensing cytochrome P450 1A1 activity by a resorufin-based isoform-specific fluorescent probe

Cytochrome P450 1A1 (CYP1A1), a heme-containing monooxygenase, is of particular importance for human health because of its vital roles in the metabolic activation of pro-carcinogenic compounds to the carcinogens. Deciphering the relevance of CYP1A1 to human diseases and screening of CYP1A1 modulators require reliable tool(s) for probing this key enzyme in complex biological matrices. Herein, a practical and ultrasensitive fluorescence-based assay for real-time sensing CYP1A1 activities in biological systems has been developed, via designing an isoform-specific fluorogenic sensor for CYP1A1 (CHPO). The newly developed fluorogenic substrate for CYP1A1 has been carefully investigated in terms of specificity, sensitivity, precision, quantitative linear range and the anti-interference ability. The excellent selectivity, strong anti-interference ability and fast response kinetics, making the practicability of CHPO-based CYP1A1 activity assay is better than that of most reported CYP1A1 activity assays. Furthermore, CHPO has been successfully used for imaging CYP1A1 activities in living cells and human tissues, as well as for high-throughput screening of CYP1A1 inhibitors using tissue preparations as enzyme sources. Collectively, this study provided a practical fluorogenic sensor for real-time sensing CYP1A1 in complex biological systems, which would strongly facilitate the investigations on the relevance of CYP1A1 to human diseases and promote high-throughput screening of CYP1A1 modulators for biomedical applications.     

Targeting programmed cell death 4 (PDCD4) with biogenic compounds in ARDS by Gaussian process‐based QSAR virtual screening

The programmed cell death 4 (PDCD4) has recently been recognized as a new and attractive target of acute respiratory distress syndrome. Here, we attempted to discover new and potent PDCD4 mediator ligands from biogenic compounds using a synthetic strategy of statistical virtual screening and experimental affinity assay. In the procedure, a Gaussian process‐based quantitative structure‐activity relationship regression predictor was developed and validated statistically based on a curated panel of structure‐based protein‐ligand affinity data. The predictor was integrated with pharmacokinetics analysis, chemical redundancy reduction, and flexible molecular docking to perform high‐throughput virtual screening against a distinct library of chemically diverse, drug‐like biogenic compounds. Consequently, 6 hits with top scores were selected, and their binding affinities to the recumbent protein of human PDCD4 were identified, 3 out of which were determined to have high or moderate affinity with K_d at micromolar level. Structural analysis of protein‐ligand complexes revealed that hydrophobic interactions and van der Waals contacts are the primary chemical forces to stabilize the complex architecture of PDCD4 with these mediator ligands, while few hydrogen bonds, salt bridges, and/or π‐π stacking at the complex interfaces confer selectivity and specificity for the protein‐ligand recognition. It is suggested that the statistical Gaussian process‐based quantitative structure‐activity relationship screening strategy can be successfully applied to rational discovery of biologically active compounds. The newly identified molecular entities targeting PDCD4 are considered as promising lead scaffolds to develop novel chemical therapeutics for acute respiratory distress syndrome.     

Discovery of novel wee1 inhibitors via structure-based virtual screening and biological evaluation

Wee1 plays a critical role in the arrest of G2/M cell cycle for DNA repair before entering mitosis. Many cancer cells have been identified as overexpression of Wee1. In this research, pharmacophore modeling, molecular docking and molecular dynamics simulation approaches were constructed to identify novel potential Wee1 inhibitors. A compound 8 was found to have a novel skeleton against Wee1 with an IC₅₀ value of 22.32 µM and a K_i value of 13.11 µM. Kinetic assays were employed to evaluate the compound 8 as a competitive inhibitor. Compound 8 was tested against A-549 tumor cell lines with IC₅₀ value of 17.8 µM. To investigate the intermolecular interaction of Wee1 and compound 8, further molecular dynamics simulations were performed. It indicates that the binding mode of compound 8 and reference ligand is similar. The active core scaffold of compound 8 could represent a promising lead compound for studying Wee1 and be used for further structural optimization to design more potent Wee1 inhibitors.     

Discovery of novel IDO1 inhibitors via structure-based virtual screening and biological assays

Indoleamine 2,3-dioxygenase 1 (IDO1) is a heme-containing enzyme that catalyzes the first and rate-limiting step in catabolism of tryptophan via the kynurenine pathway, which plays a pivotal role in the proliferation and differentiation of T cells. IDO1 has been proven to be an attractive target for many diseases, such as breast cancer, lung cancer, colon cancer, prostate cancer, etc. In this study, docking-based virtual screening and bioassays were conducted to identify novel inhibitors of IDO1. The cellular assay demonstrated that 24 compounds exhibited potent inhibitory activity against IDO1 at micromolar level, including 8 compounds with IC₅₀ values below 10 μM and the most potent one (compound 1) with IC₅₀ of 1.18?±?0.04 μM. Further lead optimization based on similarity searching strategy led to the discovery of compound 28 as an excellent inhibitor with IC₅₀ of 0.27?±?0.02 μM. Then, the structure–activity relationship of compounds 1, 2, 8 and 14 analogues is discussed. The interaction modes of two compounds against IDO1 were further explored through a Python Based Metal Center Parameter Builder (MCPB.py) molecular dynamics simulation, binding free energy calculation and electrostatic potential analysis. The novel IDO1 inhibitors of compound 1 and its analogues could be considered as promising scaffold for further development of IDO1 inhibitors.