Evolutionary chemical binding similarity approach integrated with 3D-QSAR method for effective virtual screening

An investigation of binding interactions of tumor-targeted peptide conjugated polyphenols with the kinase domain of ephrin B4 and B2 receptors

Recent studies have shown that Ephrin receptors may be upregulated in several types of cancers including breast, ovarian and endometrial cancers, making them a target for drug design. In this work, we have utilized a target-hopping approach to design new natural product-peptide conjugates and examined their interactions with the kinase-binding domain of EphB4 and EphB2 receptors. The peptide sequences were generated through point mutations of the known EphB4 antagonist peptide TNYLFSPNGPIA. Their anticancer properties and secondary structures were analyzed computationally. Conjugates of most optimum of peptides were then designed by binding the N-terminal of the peptides with the free carboxyl group of the polyphenols sinapate, gallate and coumarate, which are known for their inherent anticancer properties. To investigate if these conjugates have a potential to bind to the kinase domain, we carried out docking studies and MMGBSA free energy calculations of the trajectories based on the molecular dynamics simulations, with both the apo and the ATP bound kinase domains of both receptors. In most cases binding interactions occurred within the catalytic loop region, while in some cases the conjugates were found to spread out across the N-lobe and the DFG motif region. The conjugates were further tested for prediction of pharmacokinetic properties using ADME studies. Our results indicated that the conjugates were lipophilic and MDCK permeable with no CYP interactions. These findings provide an insight into the molecular interactions of these peptides and conjugates with the kinase domain of the EphB4 and EphB2 receptor. As a proof of concept, we synthesized and carried out SPR analysis with two of the conjugates (gallate-TNYLFSPNGPIA and sinapate-TNYLFSPNGPIA). Results indicated that the conjugates showed higher binding with the EphB4 receptor and minimal binding to EphB2 receptor. Sinapate-TNYLFSPNGPIA showed inhibitory activity against EphB4. These studies reveal that some of the conjugates may be developed for further investigation into in vitro and in vivo studies and potential development as therapeutics.

Iterative machine learning-based chemical similarity search to identify novel chemical inhibitors

Machine learning-based chemical screening has made substantial progress in recent years. However, these predictions often have low accuracy and high uncertainty when identifying new active chemical scaffolds. Hence, a high proportion of retrieved compounds are not structurally novel. In this study, we proposed a strategy to address this issue by iteratively optimizing an evolutionary chemical binding similarity (ECBS) model using experimental validation data. Various data update and model retraining schemes were tested to efficiently incorporate new experimental data into ECBS models, resulting in a fine-tuned ECBS model with improved accuracy and coverage. To demonstrate the effectiveness of our approach, we identified the novel hit molecules for the mitogen-activated protein kinase kinase 1 (MEK1). These molecules showed sub-micromolar affinity (Kd 0.1-5.3 μM) to MEKs and were distinct from previously-known MEK1 inhibitors. We also determined the binding specificity of different MEK isoforms and proposed potential docking models. Furthermore, using de novo drug design tools, we utilized one of the new MEK inhibitors to generate additional drug-like molecules with improved binding scores. This resulted in the identification of several potential MEK1 inhibitors with better binding affinity scores. Our results demonstrated the potential of this approach for identifying novel hit molecules and optimizing their binding affinities.

Network pharmacology based anti-diabetic attributes of bioactive compounds from Ocimum gratissimum L. through computational approach

The present research was framed to determine the key compounds present in the plant Ocimum gratissimum L. targeting protein molecules of Diabetes Mellitus (DM) by employing In-silico approaches. Phytochemicals previously reported to be present in this herb were collated through literature survey and public phytochemical databases, and their probable targets were anticipated using BindingDB (p ≥ 0.7). STRING and KEGG pathway databases were employed for pathway enrichment analysis. Homology modelling was executed to elucidate the structures of therapeutic targets. Further, Phytocompounds from O. gratissimum were subjected for docking with four therapeutic targets of DM by using AutoDock vina through POAP pipeline implementation. 30 compounds were predicted to target 136 protein molecules including aldose reductase, DPP4, alpha-amylase, and alpha-glucosidase. Neuroactive ligand-receptor interaction, MAPK, PI3K-Akt, starch and insulin resistance were predicted to have potentially modulation by phytocompounds. Based on the phytocompound's binding score with the four targets of DM, Rutin scored the lowest binding energy (-11 kcal/mol) with Aldose reductase by forming 17 intermolecular interactions. In conclusion, based on the network and binding score, phytocompounds from O. gratissimum have a synergistic and considerable effect in the management of DM via multi-compound, multi-target, and multi-pathway mechanisms.

Preliminary modelling as the first stage of targeted organic synthesis

The review aims to present a classification and applicability analysis of methods for preliminary molecular modelling for targeted organic, catalytic and biocatalytic synthesis. The following three main approaches are considered as a primary classification of the methods: modelling of the target – ligand coordination without structural information on both the target and the resulting complex; calculations based on experimentally obtained structural information about the target; and dynamic simulation of the target – ligand complex and the reaction mechanism with calculation of the free energy of the reaction. The review is meant for synthetic chemists to be used as a guide for building an algorithm for preliminary modelling and synthesis of structures with specified properties.

The bibliography includes 353 references.

Recent advances in, and challenges of, anti-angiogenesis agents for tumor chemotherapy based on vascular normalization

A major problem associated with cancer treatment is resistance-prone chemotherapeutic drugs. An increasing number of studies have documented that the occurrence of resistance tends to be associated with abnormal blood vessels. In 2001, Jain proposed the vascular normalization theory, which was recently applied to the drug-resistant treatment of tumors in the clinic. Through the intervention of angiogenesis inhibitors, remodeling the structure and function of abnormal vessels can maximize the efficacy of chemotherapeutic drugs. In this review, we systematically describe the occurrence and progress of tumor angiogenesis, as well as the pathological characteristics of tumor blood vessels. Moreover, druggable targets for vascular normalization and the development of related inhibitors are also outlined.

Tropolone-Bearing Sesquiterpenes from Juniperus chinensis: Structures, Photochemistry and Bioactivity

The tropolone-bearing sesquiterpenes juniperone A (1) and norjuniperone A (2) were isolated from the folk medicinal plant Juniperus chinensis, and their structures were determined by a combination of spectroscopic and crystallographic methods. Photojuniperones A1 (3) and A2 (4), bearing bicyclo[3,2,0]heptadienones derived from tropolone, were photochemically produced and structurally identified by spectroscopic methods. Predicted by the machine learning-based assay, 1 significantly inhibited the action of tyrosinase. The new compounds also inhibited lipid accumulation and enhanced the extracellular glycerol excretion.

Combined mass spectrometry-guided genome mining and virtual screening for acaricidal activity in secondary metabolites of Bacillus velezensis W1

A comprehensive analytic strategy was performed to study the acaricidal activity ingredients of Bacillus velezensis W1, a strain for biological control of Tetranychus urticae. Through genome mining, 14 biosynthetic gene clusters were identified, which encode secondary metabolites, and these were further confirmed by MALDI-TOF-MS or LC-ESI-MS/MS, including bacillomycin D C13–C17, macrolactin A, 7-O-malonyl-macrolactin A, surfactin C14, and surfactin C15. Moreover, 27 volatile compounds were identified by GC-MS, mainly including cyclodipeptides, alkanes, organic acids, and esters. Finally, 43 compounds identified from W1 were used in the virtual screening of acaricidal activity. The results showed that 16 compounds, including cyclodipeptides, bacillomycins, macrolactins, and surfactins, have acaricidal potential. This work provides a base for studying the mechanism of acaricidal action of B. velezensis W1 and a comprehensive strategy for the study of active ingredients from biocontrol strains.

A comprehensive analytic strategy was performed to study the acaricidal activity ingredients of Bacillus velezensis W1, a strain for biological control of Tetranychus urticae.

Identification of Tyrosinase Inhibitors and Their Structure-Activity Relationships via Evolutionary Chemical Binding Similarity and Structure-Based Methods

Tyrosinase is an enzyme that plays a crucial role in the melanogenesis of humans and the browning of food products. Thus, tyrosinase inhibitors that are useful to the cosmetic and food industries are required. In this study, we have used evolutionary chemical binding similarity (ECBS) to screen a virtual chemical database for human tyrosinase, which resulted in seven potential tyrosinase inhibitors confirmed through the tyrosinase inhibition assay. The tyrosinase inhibition percentage for three of the new actives was over 90% compared to 61.9% of kojic acid. From the structural analysis through pharmacophore modeling and molecular docking with the human tyrosinase model, the pi–pi interaction of tyrosinase inhibitors with conserved His367 and the polar interactions with Asn364, Glu345, and Glu203 were found to be essential for tyrosinase–ligand interactions. The pharmacophore features and the docking models showed high consistency, revealing the possible essential binding interactions of inhibitors to human tyrosinase. We have also presented the activity cliff analysis that successfully revealed the chemical features related to substantial activity changes found in the new tyrosinase inhibitors. The newly identified inhibitors and their structure–activity relationships presented here will help to identify or design new human tyrosinase inhibitors.