Comparative evaluation of several docking tools for docking small molecule ligands to DC-SIGN

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.

In Silico Studies of Four Compounds of Cecropia obtusifolia against Malaria Parasite

Malaria is a disease that affects many people in the world. In Mexico, malaria remains an active disease in certain regions, particularly in the states of Chiapas and Chihuahua. While antimalarial effects have been attributed to some species of Cecropia in various countries, no such studies have been conducted in Mexico. Therefore, the objective of this study was to evaluate the in silico antimalarial activity of some active compounds identified according to the literature in the species of Cecropia obtusifolia, belonging to the Cecropiaceae family, such as ursolic acid, α-amyrin, chrysin, and isoorientin. These compounds were evaluated with specific molecular docking and molecular dynamics (MD) studies using three different malarial targets with the PDB codes 1CET, 2BL9, and 4ZL4 as well as the prediction of their pharmacokinetic (Pk) properties. Docking analysis revealed the following best binding energies (kcal/mol): isoorientin-1CET (-9.1), isoorientin-2BL9 (-8.8), and chrysin-4ZL4 (-9.6). MD simulation validated the stability of the complexes. Pharmacokinetics analysis suggested that the compounds would generally perform well if administered. Therefore, these results suggest that these compounds may be used as potential drugs for the treatment of malaria.

Network pharmacology and molecular docking analysis on potential molecular targets and mechanism of action of BRAF inhibitors for application in wound healing

Topical application of BRAF inhibitors has been shown to accelerate wound healing in murine models, which can be extrapolated into clinical applications. The aim of the study was to identify suitable pharmacological targets of BRAF inhibitors and elucidate their mechanisms of action for therapeutic applicability in wound healing, by employing bioinformatics tools including network pharmacology and molecular docking. The potential targets for BRAF inhibitors were obtained from SwissTargetPrediction, DrugBank, CTD, Therapeutic Target Database, and Binding Database. Targets of wound healing were obtained using online databases DisGeNET and OMIM (Online Mendelian Inheritance in Man). Common targets were found by using the online GeneVenn tool. Common targets were then imported to STRING to construct interaction networks. Topological parameters were assessed using Cytoscape and core targets were identified. FunRich was employed to uncover the signaling pathways, cellular components, molecular functions, and biological processes in which the core targets participate. Finally, molecular docking was performed using MOE software. Key targets for the therapeutic application of BRAF inhibitors for wound healing are peroxisome proliferator-activated receptor γ, matrix metalloproteinase 9, AKT serine/threonine kinase 1, mammalian target of rapamycin, and Ki-ras2 Kirsten rat sarcoma viral oncogene homolog. The most potent BRAF inhibitors that can be exploited for their paradoxical activity for wound healing applications are Encorafenib and Dabrafenib. By using network pharmacology and molecular docking, it can be predicted that the paradoxical activity of BRAF inhibitors can be used for their potential application in wound healing.

Discovery of a New Drug-Like Series of OGT Inhibitors by Virtual Screening

O-GlcNAcylation is an essential post-translational modification installed by the enzyme O-β-N-acetyl-d-glucosaminyl transferase (OGT). Modulating this enzyme would be extremely valuable to better understand its role in the development of serious human pathologies, such as diabetes and cancer. However, the limited availability of potent and selective inhibitors hinders the validation of this potential therapeutic target. To explore new chemotypes that target the active site of OGT, we performed virtual screening of a large library of commercially available compounds with drug-like properties. We purchased samples of the most promising virtual hits and used enzyme assays to identify authentic leads. Structure-activity relationships of the best identified OGT inhibitor were explored by generating a small library of derivatives. Our best hit displays a novel uridine mimetic scaffold and inhibited the recombinant enzyme with an IC50 value of 7 µM. The current hit represents an excellent starting point for designing and developing a new set of OGT inhibitors that may prove useful for exploring the biology of OGT.

An Efficient ABC_DE_Based Hybrid Algorithm for Protein-Ligand Docking

Protein–ligand docking is a process of searching for the optimal binding conformation between the receptor and the ligand. Automated docking plays an important role in drug design, and an efficient search algorithm is needed to tackle the docking problem. To tackle the protein–ligand docking problem more efficiently, An ABC_DE_based hybrid algorithm (ADHDOCK), integrating artificial bee colony (ABC) algorithm and differential evolution (DE) algorithm, is proposed in the article. ADHDOCK applies an adaptive population partition (APP) mechanism to reasonably allocate the computational resources of the population in each iteration process, which helps the novel method make better use of the advantages of ABC and DE. The experiment tested fifty protein–ligand docking problems to compare the performance of ADHDOCK, ABC, DE, Lamarckian genetic algorithm (LGA), running history information guided genetic algorithm (HIGA), and swarm optimization for highly flexible protein–ligand docking (SODOCK). The results clearly exhibit the capability of ADHDOCK toward finding the lowest energy and the smallest root-mean-square deviation (RMSD) on most of the protein–ligand docking problems with respect to the other five algorithms.

The discovery of a novel compound with potent antitumor activity: virtual screening, synthesis, biological evaluation and preliminary mechanism study

Farnesyltransferase has been regarded as a promising drug target against cancer as it is critical for membrane association of several signal transduction proteins. In this study, a novel farnesyltransferase inhibitor (IMB-1406) was identified through virtual screening. It exhibits stronger potency (IC₅₀s: 6.92–8.99 μM) than Sunitinib against all of the tested cancer cell lines. Preliminary studies on mechanism reveal that IMB-1406 induces apoptosis in HepG2 cells by arresting the cell cycle at the S phase, altering anti- and pro-apoptotic proteins leading to mitochondrial dysfunction and activation of caspase-3. This anti-tumor effect is most probably related to the inhibition of farnesyltransferase as indicated by molecular docking. Overall, IMB-1406 is a novel lead compound with potent antitumor activity and deserves further structural modifications.

Computer-aided drug design of capuramycin analogues as anti-tuberculosis antibiotics by 3D-QSAR and molecular docking

Capuramycin and a few semisynthetic derivatives have shown potential as anti-tuberculosis antibiotics.To understand their mechanism of action and structureactivity relationships a 3D-QSAR and molecular docking studies were performed. A set of 52 capuramycin derivatives for the training set and 13 for the validation set was used. A highly predictive MFA model was obtained with crossvalidated q2 of 0.398, and non-cross validated partial least-squares (PLS) analysis showed a conventional r2 of 0.976 and r2pred of 0.839. The model has an excellent predictive ability. Combining the 3D-QSAR and molecular docking studies, a number of new capuramycin analogs with predicted improved activities were designed. Biological activity tests of one analog showed useful antibiotic activity against Mycobacterium smegmatis MC2 155 and Mycobacterium tuberculosis H37Rv. Computer-aided molecular docking and 3D-QSAR can improve the design of new capuramycin antimycobacterial antibiotics.

HIGA: A Running History Information Guided Genetic Algorithm for Protein-Ligand Docking

Protein-ligand docking is an essential part of computer-aided drug design, and it identifies the binding patterns of proteins and ligands by computer simulation. Though Lamarckian genetic algorithm (LGA) has demonstrated excellent performance in terms of protein-ligand docking problems, it can not memorize the history information that it has accessed, rendering it effort-consuming to discover some promising solutions. This article illustrates a novel optimization algorithm (HIGA), which is based on LGA for solving the protein-ligand docking problems with an aim to overcome the drawback mentioned above. A running history information guided model, which includes CE crossover, ED mutation, and BSP tree, is applied in the method. The novel algorithm is more efficient to find the lowest energy of protein-ligand docking. We evaluate the performance of HIGA in comparison with GA, LGA, EDGA, CEPGA, SODOCK, and ABC, the results of which indicate that HIGA outperforms other search algorithms.

Genetic algorithm with a crossover elitist preservation mechanism for protein-ligand docking

Protein–ligand docking plays an important role in computer-aided pharmaceutical development. Protein–ligand docking can be defined as a search algorithm with a scoring function, whose aim is to determine the conformation of the ligand and the receptor with the lowest energy. Hence, to improve an efficient algorithm has become a very significant challenge. In this paper, a novel search algorithm based on crossover elitist preservation mechanism (CEP) for solving protein–ligand docking problems is proposed. The proposed algorithm, namely genetic algorithm with crossover elitist preservation (CEPGA), employ the CEP to keep the elite individuals of the last generation and make the crossover more efficient and robust. The performance of CEPGA is tested on sixteen molecular docking complexes from RCSB protein data bank. In comparison with GA, LGA and SODOCK in the aspects of lowest energy and highest accuracy, the results of which indicate that the CEPGA is a reliable and successful method for protein–ligand docking problems.

Synthesis and Biological Evaluations of Ring Substituted Tetrahydroisoquinolines (THIQs) as Anti-Breast Cancer Agents

Breast cancer is a leading cause of mortality among women, resulting in more than half a million deaths worldwide every year. Although chemotherapeutic drugs remain the main stay of cancer treatment, it is observed that toxicity to normal cells poses a limitation to their therapeutic values. Moreover, the patient recovery rate from advanced breast cancer by chemotherapy is still unacceptably low. Tetrahydroisoqinoline derivatives (THIQs) were reported to act as selective subtype estrogen receptor antagonists/agonists and may serve as potential therapeutic agents for breast cancer. In continuation of previous work we systematically synthesized and characterized the tetrahydroisoquinoline (THIQs) analogs. In-vitro antiproliferative activity of new substituted tetrahydroisoquinoline analogs were evaluated against human ER (+) MCF-7 (breast), ER (−) MDA-MB-231 (breast) and Ishikawa (endometrial) cancer cell lines using the CellTiter-Glo luminescent cell viability assay. The most active compounds obtained in this study were 2b, 2i, and 3 g as demonstrated by their activity (IC₅₀=0.2 μg/mL, 0.08 μg/mL; 0.61 μg/mL, 0.09 μg/mL; 0.25 μg/mL, 0.11 μg/mL) against MCF-7 and Ishikawa cell lines respectively, in comparison to Tamoxifen activity (IC₅₀=3.99 μg/mL, 7.87 μg/ml). The newly synthesized molecules were docked in the active sites of the ER-α (PDB: 3ERT), ER-β (PDB: 1QKN) and alpha-beta tubulin taxol complex (1JFF) crystal structures to determine the probable binding modes (bioactive conformations) of the active compounds.

Discovery of unsymmetrical aromatic disulfides as novel inhibitors of SARS-CoV main protease: Chemical synthesis, biological evaluation, molecular docking and 3D-QSAR study

The worldwide outbreak of severe acute respiratory syndrome (SARS) in 2003 had caused a high rate of mortality. Main protease (M^pro) of SARS-associated coronavirus (SARS-CoV) is an important target to discover pharmaceutical compounds for the therapy of this life-threatening disease. During the course of screening new anti-SARS agents, we have identified that a series of unsymmetrical aromatic disulfides inhibited SARS-CoV M^pro significantly for the first time. Herein, 40 novel unsymmetrical aromatic disulfides were synthesized chemically and their biological activities were evaluated in vitro against SARS-CoV M^pro. These novel compounds displayed excellent IC₅₀ data in the range of 0.516–5.954 μM. Preliminary studies indicated that these disulfides are reversible and mpetitive inhibitors. A possible binding mode was generated via molecular docking simulation and a comparative field analysis (CoMFA) model was constructed to understand the structure-activity relationships. The present research therefore has provided some meaningful guidance to design and identify anti-SARS drugs with totally new chemical structures.

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40 novel unsymmetrical aromatic disulfides were synthesized.

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The synthesized disulfide compounds are potent inhibitors of SARS main protease.

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Possible binding mode and structure-activity relationships of the compounds were established.

The Comparison of Docking Search Algorithms and Scoring Functions

This chapter, composed of two parts, firstly provides molecular docking overview and secondly two molecular docking case studies are described. In overview, basic information about molecular docking are presented such as description of search algorithms and scoring functions applied in various docking programs. Brief description of methods utilized in some of the most popular docking programs also applied in our experimental work is provided. AutoDock, CDOCKER, GOLD, FlexX and FRED were used for docking studies of the DC-SIGN protein, while GOLD was further used for docking studies of cathepsin K protein. Methods and results of our studies with their contribution to science and medicine are presented. Content of the chapter is therefore appropriate for public of Medicinal and Organic Chemistry as an overview of docking studies, and also for Computational Chemists at the beginning of their work as the introduction to application of molecular docking programs.

EDGA: A Population Evolution Direction-Guided Genetic Algorithm for Protein-Ligand Docking

Protein-ligand docking can be formulated as a search algorithm associated with an accurate scoring function. However, most current search algorithms cannot show good performance in docking problems, especially for highly flexible docking. To overcome this drawback, this article presents a novel and robust optimization algorithm (EDGA) based on the Lamarckian genetic algorithm (LGA) for solving flexible protein-ligand docking problems. This method applies a population evolution direction-guided model of genetics, in which search direction evolves to the optimum solution. The method is more efficient to find the lowest energy of protein-ligand docking. We consider four search methods-a tradition genetic algorithm, LGA, SODOCK, and EDGA-and compare their performance in docking of six protein-ligand docking problems. The results show that EDGA is the most stable, reliable, and successful.

STD NMR and molecular modelling insights into interaction of novel mannose-based ligands with DC-SIGN

STD-NMR and molecular modelling study of four α-d-mannosides show new contacts in DC-SIGN binding site to help develop potent DC-SIGN antagonists.