The latest automated docking technologies for novel drug discovery

Identification and characterization of a calcium-binding peptide from salmon bone for the targeted inhibition of α-amylase in digestion

α-Amylase, essential for carbohydrate digestion, relies on calcium (Ca) for its structural integrity and enzymatic activity. This study explored the inhibitory effect of salmon bone peptides on α-amylase activity through their interaction with the enzyme's Ca-binding sites. Among the various salmon bone hydrolysates, salmon bone trypsin hydrolysate (SBTH) exhibited the highest α-amylase inhibition. The peptide IEELEEELEAER (PIE), with a sequence of Ile-Glu-Glu-Leu-Glu-Glu-Glu-Glu-Leu-Glu-Ala-Glu-Arg from SBTH, was found to specifically target the Ca-binding sites in α-amylase, interacting with key residues such as Asp206, Trp203, His201, etc. Additionally, cellular experiments using 3 T3-L1 preadipocytes indicated PIE's capability to suppress adipocyte differentiation, and decreases in intracellular triglycerides, total cholesterol, and lipid accumulation. In vivo studies also showed a significant reduction in weight gain in the group treated with PIE(6.61%)compared with the control group (33.65%). These findings suggest PIE is an effective α-amylase inhibitor, showing promise for obesity treatment.

Predicting binding events in very flexible, allosteric, multi-domain proteins

Knowledge of the structures formed by proteins and small ligands is of fundamental importance for understanding molecular principles of chemotherapy and for designing new and more effective drugs. Due to the still high costs and to the several limitations of experimental techniques, it is most often desirable to predict these ligand-protein complexes in silico, particularly when screening for new putative drugs from databases of millions of compounds. While virtual screening based on molecular docking is widely used for this purpose, it generally fails in mimicking binding events associated with large conformational changes in the protein, particularly when the latter involve multiple domains. In this work, we describe a new methodology aimed at generating bound-like conformations of very flexible and allosteric proteins bearing multiple binding sites. Validation was performed on the enzyme adenylate kinase (ADK), a paradigmatic example of proteins that undergo very large conformational changes upon ligand binding. By only exploiting the unbound structure and the putative binding sites of the protein, we generated a significant fraction of bound-like structures, which employed in ensemble-docking calculations allowed to find native-like poses of substrates, inhibitors, and catalytically incompetent binders. Our protocol provides a general framework for the generation of bound-like conformations of flexible proteins that are suitable to host different ligands, demonstrating high sensitivity to the fine chemical details that regulate protein's activity. We foresee applications in virtual screening for difficult targets, prediction of the impact of amino acid mutations on structure and dynamics, and protein engineering.

Prediction of potential targets and toxicological insights of Astragalus in liver cancer based on network pharmacology: Integrating systems biology, drug interaction networks, and toxicological perspectives

This study investigates Astragalus's efficacy as a novel therapeutic option for primary liver cancer (PLC), capitalizing on its anti-inflammatory and antiviral effects. We utilized network pharmacology to unveil Astragalus's potential targets against PLC, revealing significant gene expression alterations in treated samples-20 genes were up-regulated, and 20 were down-regulated compared to controls. Our analysis extended to single-cell resolution, where we processed scRNA-seq data to discern 15 unique cell clusters within the immune, malignant, and stromal compartments through advanced algorithms like UMAP and tSNE. To delve deeper into the functional implications of these gene expression changes, we conducted comprehensive gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses, alongside Gene Set Variation Analysis, to elucidate the biological processes and pathways involved. Further, we constructed protein-protein interaction networks to visualize the intricate molecular interplay, highlighting the down-regulation of MT1E in PLC cells, a finding corroborated by quantitative polymerase chain reaction. Molecular docking studies affirmed the potent interaction between Astragalus's active compounds and MT proteins, underscoring a targeted therapeutic mechanism. Our investigation also encompassed a detailed cellular landscape analysis, identifying nine cell subgroups related to MT1 expression and specifying five cell subsets through the SingleR package. Advanced trajectory and cell-cell interaction analyses offered deeper insights into the dynamics of MT1-associated cellular subpopulations. This comprehensive methodology not only underpins Astragalus's promising role in PLC treatment but also advances our understanding of its molecular and cellular mechanisms, paving the way for targeted therapeutic strategies.

Regulation and mechanism of organic selenium on quorum sensing, biofilm, and antioxidant effects of Lactobacillus paracasei

Different organic compounds can have varying degrees of impact on the activity of Lactobacillus paracasei. The study focused on the impact and action mechanism of different organic selenium products on the bioactivity of two strains of L. paracasei. The growth, antioxidant activity, extracellular polysaccharide secretion, quorum sensing (QS), and biofilm formation of the strains before and after the addition of organic selenium crude products and three organic selenium standard were evaluated. The results showed that the addition of crude organic selenium promoted the various activities of the strain. l-selenocysteine had the strongest regulatory effect, with maximum GIM1.80 biofilm formation when it reached a critical concentration of 0.4 μg/mL; l-selenomethionine resulted in the highest activity of the signal molecule Auto inducer-2 of GDMCC1.155, when it reached a critical concentration of 0.4 μg/mL. The results of scanning electron microscopy demonstrated that the addition of organic selenium effectively improved the morphological structure of the two bacterial cells. Molecular docking revealed that the mechanism by which organic selenium regulates QS in Lactobacillus was achieved by binding two crucial receptor proteins (histidine protein kinase HKP and periplasmic binding protein LuxP) from specific sites. Furthermore, organic selenium products have a beneficial regulatory effect on the biological activity of L. paracasei. Overall, these findings provide a new alternative (organic selenium) for regulating the viability and beneficial activity of L. paracasei.

Biodegradation mechanisms of polycyclic aromatic hydrocarbons: Combination of instrumental analysis and theoretical calculation

Polycyclic aromatic hydrocarbons (PAHs) are a common class of petroleum hydrocarbons, widely encountered in both environment and industrial pollution sources. Owing to their toxicity, environmental persistence, and potential bioaccumulation properties, a mounting interest has been kindled in addressing the remediation of PAHs. Biodegradation is widely employed for the removal and remediation of PAHs due to its low cost, lack of second-contamination and ease of operation. This paper reviews the degradation efficiency of degradation and the underlying mechanisms exhibited by algae, bacteria, and fungi in remediation. Additionally, it delved into the application of modern instrumental analysis techniques and theoretical investigations in the realm of PAH degradation. Advanced instrumental analysis methods such as mass spectrometry provide a powerful tool for identifying intermediates and metabolites throughout the degradation process. Meanwhile, theoretical calculations could guide the optimization of degradation processes by revealing the reaction mechanisms and energy changes in PAH degradation. The combined use of instrumental analysis and theoretical calculations allows for a comprehensive understanding of the degradation mechanisms of PAHs and provides new insights and approaches for the development of environmental remediation technologies.

GM1a ganglioside-binding domain peptide inhibits host adhesion and inflammatory response of enterotoxigenic Escherichia coli heat-labile enterotoxin-B in HCT-8 cells

Enterotoxigenic Escherichia coli (ETEC) is a major cause of illness and death but has no effective therapy. The heat-labile enterotoxin LT is a significant virulence factor produced by ETEC. The heat-labile enterotoxin-B (LT-B) subunit may enter host cells by binding to monosialotetrahexosylganglioside-a (GM1a), a monosialoganglioside found on the plasma membrane surface of animal epithelial cells. This research was conducted to develop conformationally comparable peptides to the carbohydrate epitope of GM1a for the treatment of ETEC. We used the LT-B subunit to select LT-B-binding peptides that structurally resemble GM1a. The ganglioside microarray and docking simulations were used to identify three GM1a ganglioside-binding domain (GBD) peptides based on LT-B recognition. Peptides had an inhibiting effect on the binding of LT-B to GM1a. The binding capacity, functional inhibitory activity, and in vitro effects of the GBD peptides were evaluated using HCT-8 cells, a human intestinal epithelial cell line, to evaluate the feasibility of deploying GBD peptides to combat bacterial infections. KILSYTESMAGKREMVIIT was the most efficient peptide in inhibiting cellular absorption of LT-B in cells. Our findings offer compelling evidence that GM1a GBD-like peptides might act as new therapeutics to inhibit LT-B binding to epithelial cells and avoid the subsequent physiological consequences of LT.

Computational study of the binding orientation and affinity of noncovalent inhibitors of the papain-like protease (PLpro) from SARS-CoV-1 considering the protein flexibility by using molecular dynamics and cross-docking

The papain-like protease (PLpro) from zoonotic coronaviruses (CoVs) has been identified as a target with an essential role in viral respiratory diseases caused by Severe Acute Respiratory Syndrome-associated coronaviruses (SARS-CoVs). The design of PLpro inhibitors has been proposed as an alternative to developing potential drugs against this disease. In this work, 67 naphthalene-derived compounds as noncovalent PLpro inhibitors were studied using molecular modeling methods. Structural characteristics of the bioactive conformations of these inhibitors and their interactions at the SARS-CoV-1 PLpro binding site were reported here in detail, taking into account the flexibility of the protein residues. Firstly, a molecular docking protocol was used to obtain the orientations of the inhibitors. After this, the orientations were compared, and the recurrent interactions between the PLpro residues and ligand chemical groups were described (with LigRMSD and interaction fingerprints methods). In addition, efforts were made to find correlations between docking energy values and experimentally determined binding affinities. For this, the PLpro was sampled by using Gaussian Accelerated Molecular Dynamics (GaMD), generating multiple conformations of the binding site. Diverse protein conformations were selected and a cross-docking experiment was performed, yielding models of the 67 naphthalene-derived compounds adopting different binding modes. Representative complexes for each ligand were selected to obtain the highest correlation between docking energies and activities. A good correlation (R ² = 0.948) was found when this flexible docking protocol was performed.

Dock-able linear and homodetic di, tri, tetra and pentapeptide library from canonical amino acids: SARS-CoV-2 Mpro as a case study

Peptide-based therapeutics are increasingly pushing to the forefront of biomedicine with their promise of high specificity and low toxicity. Although noncanonical residues can always be used, employing only the natural 20 residues restricts the chemical space to a finite dimension allowing for comprehensive in silico screening. Towards this goal, the dataset comprising all possible di-, tri-, and tetra-peptide combinations of the canonical residues has been previously reported. However, with increasing computational power, the comprehensive set of pentapeptides is now also feasible for screening as the comprehensive set of cyclic peptides comprising four or five residues. Here, we provide both the complete and prefiltered libraries of all di-, tri-, tetra-, and penta-peptide sequences from 20 canonical amino acids and their homodetic (N-to-C-terminal) cyclic homologues. The FASTA, simplified molecular-input line-entry system (SMILES), and structure-data file (SDF)-three dimension (3D) libraries can be readily used for screening against protein targets. We also provide a simple method and tool for conducting identity-based filtering. Access to this dataset will accelerate small peptide screening workflows and encourage their use in drug discovery campaigns. As a case study, the developed library was screened against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease to identify potential small peptide inhibitors.

Investigation of pharmacodynamic material basis of Anemarrhenae Rhizoma and its processed products based on plant metabolomics and molecular docking technology

RATIONALE

Anemarrhenae Rhizoma (AR) has been an often used traditional Chinese medicine (TCM) for a long time. Its salt-processed form is one of the most common application forms. Modern pharmacological research has shown that the salt-processed product has various significantly enhanced pharmacological activities. However, the pharmacodynamic material basis of this change is not yet known. The aim of this study was to develop a strategy to screen pharmacodynamic substances in AR and salt-processed AR (SAR).

METHODS

An integrated strategy combining plant metabolomics with molecular docking technology was established to screen pharmacodynamic substances. The plant metabolomics analysis was performed to select the chemical markers between AR and SAR. Then, molecular docking technology was applied to explore the relationship between chemical markers and diabetes targets (α-glucosidase). Finally, potential quality control markers were screened.

RESULTS

There were significant differences in the quantification of nine steroidal saponins between AR and SAR. The results of plant metabolomics analysis showed a quite clear discrimination including 29 chemical markers between AR and SAR. Taking the hypoglycemic activity into consideration, 16 steroidal saponins were selected as potential quality markers.

CONCLUSIONS

The developed method not only supplied an optional solution to search for pharmacophores in AR and SAR, but also provided a foundation for the study of the differential components and pharmacodynamics in various processed products of TCMs.

Enantioseparation and mechanism study on baclofen by capillary electrophoresis and molecular modeling

Enantioselective analysis of chiral drugs plays a significant role in chemistry, biology and pharmacology. Baclofen, an antispasmodic chiral drug, has been widely studied due to the obvious differences in toxicity and medical activity between enantiomers. Herein, a simple and efficient method for separation of baclofen enantiomers by capillary electrophoresis was established without complicated sample derivatization and expensive instruments. Then, the molecular modeling and density functional theory were used to simulate and investigate the chiral resolution mechanism of electrophoresis, the calculated intermolecular forces were directly presented by visualization softwares. Moreover, the theoretical and experimental electronic circular dichroism (ECD) spectra of ionized baclofen were compared, and the configuration of dominant enantiomer in the nonracemic mixture can be determined by ECD signal intensity, which was proportional to the electrophoresis peak area difference of the corresponding enantiomer excess experiments. In this way, the peak order identification and configuration quantification of baclofen enantiomers in electrophoretic separation were successfully achieved without relying on a single standard.

Design, synthesis and antitumor activity of 5-trifluoromethylpyrimidine derivatives as EGFR inhibitors

A new series of 5-trifluoromethylpyrimidine derivatives were designed and synthesised as EGFR inhibitors. Three tumour cells A549, MCF-7, PC-3 and EGFR kinase were employed to evaluate their biological activities. The results were shown that most of the target compounds existed excellent antitumor activities. In particular, the IC₅₀ values of compound 9u (E)-3-((2-((4-(3-(3-fluorophenyl)acrylamido)phenyl)amino)-5-(trifluoromethyl)pyrimidin-4-yl)amino)-N-methylthiophene-2-carboxamide against A549, MCF-7, PC-3 cells and EGFR kinase reached to 0.35 μM, 3.24 μM, 5.12 μM, and 0.091 μM, respectively. Additionally, further researches revealed that compound 9u could induce early apoptosis of A549 cells and arrest the cells in G2/M phase. Taken together, these findings indicated that compound 9u was potential for developing as antitumor reagent.

Exploring the Effective Components and Mechanism of Action of Japanese Ardisia in the Treatment of Autoimmune Hepatitis Based on Network Pharmacology and Experimental Verification

Japanese Ardisia is widely used as a hepatoprotective and anti-inflammatory agent in China. However, the active ingredients in Japanese Ardisia and their potential mechanisms of action in the treatment of autoimmune hepatitis (AIH) are unknown. The pharmacodynamic substance and mechanism of action of Japanese Ardisia in the treatment of AIH were investigated using network pharmacology and molecular docking technology in this study. Following that, the effects of Japanese Ardisia were evaluated using the concanavalin A (Con A)-induced acute liver injury rat model. The active ingredients and targets of Japanese Ardisia were searched using the Traditional Chinese Medicine Systems Pharmacology database, and hepatitis-related therapeutic targets were identified through GeneCards and Online Mendelian Inheritance in Man databases. A compound-target network was then constructed using Cytoscape software, and enrichment analysis was performed using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Molecular docking technology was used to simulate the docking of key targets, and the AIH rat model was used to validate the expression of key targets. Nineteen active chemical components and 143 key target genes were identified. GO enrichment analysis revealed that the treatment of AIH with Japanese Ardisia mainly involved DNA-binding transcription factor binding, RNA polymerase II-specific DNA transcription factor binding, cytokine receptor binding, receptor-ligand activity, ubiquitin-like protein ligase binding, and cytokine activity. In the KEGG enrichment analysis, 165 pathways were identified, including the lipid and atherosclerotic pathway, IL-17 signaling pathway, TNF signaling pathway, hepatitis B pathway, and the AGE-RAGE signaling pathway in diabetic complications. These pathways may be the key to effective AIH treatment with Japanese Ardisia. Molecular docking showed that quercetin and kaempferol have good binding to AKT1, IL6, VEGFA, and CASP3. Animal experiments demonstrated that Japanese Ardisia could increase the expression of AKT1 and decrease the expression of CASP3 protein, as well as IL-6, in rat liver tissues. This study identified multiple molecular targets and pathways for Japanese Ardisia in the treatment of AIH. At the same time, the effectiveness of Japanese Ardisia in treating AIH was verified by animal experiments.

Combined Bioinformatics and Combinatorial Chemistry Tools to Locate Drug-Able Anti-TB Phytochemicals: A Cost-Effective Platform for Natural Product-Based Drug Discovery

Based on extensive experimental studies, a huge number of phytochemicals showed potential activity against tuberculosis (TB) at a lower minimum inhibitory concentration (MIC) and fewer toxicity profiles. However, these promising drugs have not been able to convert from 'lead' to 'mainstream' due to inadequate drug-ability profiles. Thus, early drug-prospective analyses are required at the primary stage to accelerate natural-product-based drug discovery with limited resources and time. In the present study, we have selected seventy-three potential anti-TB phytochemicals (MIC value ≤10 μg/mL) and assessed the drug-ability profiles using bioinformatics and combinatorial chemistry tools, systematically. Primarily, the molecular docking study was done against two putative drug targets, catalase-peroxidase enzyme (katG) and RNA polymerase subunit-β (rpoB) of Mycobacterium tuberculosis (Mtb) using AutoDock 4.2 software. Further, assessed the drug-ability score from Molsoft, toxicity profiles from ProTox, pharmacokinetics from SwisADME, hierarchical cluster analysis (HCA) by ChemMine tools and frontier molecular orbitals (FMOs) with Avogadro and structural activity relationships (SAR) analysis with ChemDraw 18.0 software. Above analyses indicated that, lower MIC exhibited anti-TB phytochemicals, abietane, 12-demethylmulticaulin exhibited poor docking and drug-ability scores, while tiliacorinine, 2-nortiliacorinine showed higher binding energy and drug-ability profiles. Overall, tiliacorinine, 2-nortiliacorinine, 7α-acetoxy-6β-hydroxyroyleanone (AHR), (2S)-naringenin and isovachhalcone were found as the most active and drug-able anti-TB candidates from 73 candidates. Phytochemicals are always a vital source of mainstream drugs, but the MIC value of a phytochemical is not sufficient for it to be promoted. An ideal drug-ability profile is therefore essential for achieving clinical success, where advanced bioinformatics tools help to assess and analyse that profile. Additionally, several natural pharmacophores found in existing anti-TB drugs in SAR analyses also provide crucial information for developing potential anti-TB drug. As a conclusion, combined bioinformatics and combinatorial chemistry are the most effective strategies to locate potent-cum-drug-able candidates in the current drug-development module.

PacDOCK: A Web Server for Positional Distance-Based and Interaction-Based Analysis of Docking Results

Molecular docking is a key method for structure-based drug design used to predict the conformations assumed by small drug-like ligands when bound to their target. However, the evaluation of molecular docking studies can be hampered by the lack of a free and easy to use platform for the complete analysis of results obtained by the principal docking programs. To this aim, we developed PacDOCK, a freely available and user-friendly web server that comprises a collection of tools for positional distance-based and interaction-based analysis of docking results, which can be provided in several file formats. PacDOCK allows a complete analysis of molecular docking results through root mean square deviation (RMSD) calculation, molecular visualization, and cluster analysis of docked poses. The RMSD calculation compares docked structures with a reference structure, also when atoms are randomly labelled, and their conformational and positional differences can be visualised. In addition, it is possible to visualise a ligand into the target binding pocket and investigate the key receptor–ligand interactions. Moreover, PacDOCK enables the clustering of docking results by identifying a restrained number of clusters from many docked poses. We believe that PacDOCK will contribute to facilitating the analysis of docking results to improve the efficiency of computer-aided drug design.

Multi-Level High-Throughput Screening for Discovery of Ligands That Inhibit Insulin Aggregation

We have developed a multi-level virtual screening protocol to identify lead molecules from the FDA inactives database that can inhibit insulin aggregation. The method is based on the presence of structural and interaction specificity in non-native aggregation pathway protein-protein interactions. Some key challenges specific to the present problem, when compared with native protein association, include structural heterogeneity of the protein species involved, multiple association pathways, and relatively higher probability of conformational rearrangement of the association complex. In this multi-step method, the inactives database was first screened using the dominant pharmacophore features of previously identified molecules shown to significantly inhibit insulin aggregation nucleation by binding to its aggregation-prone conformers. We then performed ensemble docking of several low-energy ligand conformations on these aggregation-prone conformers followed by molecular dynamics simulations and binding affinity calculations on a subset of docked complexes to identify a final set of five potential lead molecules to inhibit insulin aggregation nucleation. Their effect on aggregation inhibition was extensively investigated by incubating insulin under aggregation-prone aqueous buffer conditions (low pH, high temperature). Aggregation kinetics were characterized using size exclusion chromatography and Thioflavin T fluorescence assay, and the secondary structure was determined using circular dichroism spectroscopy. Riboflavin provided the best aggregation inhibition, with 85% native monomer retention after 48 h incubation under aggregation-prone conditions, whereas the no-ligand formulation showed complete monomer loss after 36 h. Further, insulin incubated with two of the screened inactives (aspartame, riboflavin) had the characteristic α-helical dip in CD spectra, while the no-ligand formulation showed a change to β-sheet rich conformations.

Structural Insights into the Substrate Transport Mechanisms in GTR Transporters through Ensemble Docking

Glucosinolate transporters (GTRs) are part of the nitrate/peptide transporter (NPF) family, members of which also transport specialized secondary metabolites as substrates. Glucosinolates are defense compounds derived from amino acids. We selected 4-methylthiobutyl (4MTB) and indol-3-ylmethyl (I3M) glucosinolates to study how GTR1 from Arabidopsis thaliana transports these substrates in computational simulation approaches. The designed pipeline reported here includes massive docking of 4MTB and I3M in an ensemble of GTR1 conformations (in both inward and outward conformations) extracted from molecular dynamics simulations, followed by clustered and substrate–protein interactions profiling. The identified key residues were mutated, and their role in substrate transport was tested. We were able to identify key residues that integrate a major binding site of these substrates, which is critical for transport activity. In silico approaches employed here represent a breakthrough in the plant transportomics field, as the identification of key residues usually takes a long time if performed from a purely wet-lab experimental perspective. The inclusion of structural bioinformatics in the analyses of plant transporters significantly speeds up the knowledge-gaining process and optimizes valuable time and resources.

Drug Design: Where We Are and Future Prospects

Medicinal chemistry is facing new challenges in approaching precision medicine. Several powerful new tools or improvements of already used tools are now available to medicinal chemists to help in the process of drug discovery, from a hit molecule to a clinically used drug. Among the new tools, the possibility of considering folding intermediates or the catalytic process of a protein as a target for discovering new hits has emerged. In addition, machine learning is a new valuable approach helping medicinal chemists to discover new hits. Other abilities, ranging from the better understanding of the time evolution of biochemical processes to the comprehension of the biological meaning of the data originated from genetic analyses, are on their way to progress further in the drug discovery field toward improved patient care. In this sense, the new approaches to the delivery of drugs targeted to the central nervous system, together with the advancements in understanding the metabolic pathways for a growing number of drugs and relating them to the genetic characteristics of patients, constitute important progress in the field.

Unravelling the molecular interactions between the SARS-CoV-2 RBD spike protein and various specific monoclonal antibodies

Vaccination against SARS-CoV-2 just started in most of the countries. However, the development of specific vaccines against SARS-CoV-2 is not the only approach to control the virus and monoclonal antibodies (mAbs) start to merit special attention as a therapeutic option to treat COVID-19 disease. Here, the main conformations and interactions between the receptor-binding domain (RBD) of spike glycoprotein of SARS-CoV-2 (S protein) with two mAbs (CR3022 and S309) and the ACE2 cell receptor are studied as the main representatives of three different epitopes on the RBD of S protein. The combined approach of 1 μs accelerated molecular dynamics (aMD) and ab-initio hybrid molecular dynamics is used to identify the most predominant interactions under physiological conditions. Results allow to determine the main receptor-binding mapping, hydrogen bonding network and salt bridges in the most populated antigen-antibody interface conformations. The deep knowledge on the protein-protein interactions involving mAbs and ACE2 receptor with the spike glycoprotein of SARS-CoV-2 increases background knowledge to speed up the development of new vaccines and therapeutic drugs.

Network Pharmacology-Based Dissection of the Comprehensive Molecular Mechanisms of the Herbal Prescription FDY003 Against Estrogen Receptor-Positive Breast Cancer

Estrogen receptor-positive breast cancer (ERPBC) is the commonest subtype of breast cancer, with a high prevalence, incidence, and mortality. Herbal drugs are increasingly being used to treat ERPBC, although their mechanisms of action are not fully understood. Therefore, in this study, we aimed to analyze the therapeutic properties of FDY003, a herbal anti-ERPBC prescription, using a network pharmacology approach. FDY003 decreased the viability of human ERPBC cells and sensitized them to tamoxifen, an endocrine drug that is widely used in the treatment of ERPBC. The network pharmacology analysis revealed 18 pharmacologically active components in FDY003 that may interact with and regulate 66 therapeutic targets. The enriched gene ontology terms for the FDY003 targets were associated with the modulation of cell survival and death, cell proliferation and growth arrest, and estrogen-associated cellular processes. Analysis of the pathway enrichment of the targets showed that FDY003 may target a variety of ERPBC-associated pathways, including the PIK3-Akt, focal adhesion, MAPK, and estrogen pathways. Overall, these data provide a comprehensive mechanistic insight into the anti-ERPBC activity of FDY003.

Application of ensemble pharmacophore-based virtual screening to the discovery of novel antimitotic tubulin inhibitors

Tubulin is a well-validated target for herbicides, fungicides, anti-parasitic, and anti-tumor drugs. Many of the non-cancer tubulin drugs bind to its colchicine site but no colchicine-site anticancer drug is available. The colchicine site is composed of three interconnected sub-pockets that fit their ligands and modify others' preference, making the design of molecular hybrids (that bind to more than one sub-pocket) a difficult task. Taking advantage of the more than eighty published X-ray structures of tubulin in complex with ligands bound to the colchicine site, we generated an ensemble of pharmacophore representations that flexibly sample the interactional space between the ligands and target. We searched the ZINC database for scaffolds able to fit several of the subpockets, such as tetrazoles, sulfonamides and diarylmethanes, selected roughly ~8000 compounds with favorable predicted properties. A Flexi-pharma virtual screening, based on ensemble pharmacophore, was performed by two different methodologies. Combining the scaffolds that best fit the ensemble pharmacophore-representation, we designed a new family of ligands, resulting in a novel tubulin modulator. We synthesized tetrazole 5 and tested it as a tubulin inhibitor in vitro. In good agreement with the design principles, it demonstrated micromolar activity against in vitro tubulin polymerization and nanomolar anti-proliferative effect against human epithelioid carcinoma HeLa cells through microtubule disruption, as shown by immunofluorescence confocal microscopy. The integrative methodology succedes in the design of new scaffolds for flexible proteins with structural coupling between pockets, thus expanding the way in which computational methods can be used as significant tools in the drug design process.

Improving Docking Power for Short Peptides Using Random Forest

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