Improvements, trends, and new ideas in molecular docking: 2012-2013 in review

Computational approach based on freely accessible tools for antimicrobial drug designR2

Antimicrobial drug development is crucial for public health, especially with the emergence of pandemics and drug resistance that prompts the search for new therapeutic resources. In this context, in silico assays consist of a valuable approach in the rational drug design because they enable a faster and more cost-effective identification of drug candidates compared to in vitro screening. However, once a potential drug is identified, in vitro and in vivo assays are essential to verify the expected activity of the compound and advance it through the subsequent stages of drug development. This work aims to outline an in silico protocol that utilizes only freely available computational tools for identifying new potential antimicrobial agents, which is also suitable in the broad spectrum of drug designR1;R2. Additionally, this paper reviews relevant computational methods in this context and provides a summary of information concerning the protein-ligand interaction.

DrugSynthMC: An Atom-Based Generation of Drug-like Molecules with Monte Carlo Search

A growing number of deep learning (DL) methodologies have recently been developed to design novel compounds and expand the chemical space within virtual libraries. Most of these neural network approaches design molecules to specifically bind a target based on its structural information and/or knowledge of previously identified binders. Fewer attempts have been made to develop approaches for de novo design of virtual libraries, as synthesizability of generated molecules remains a challenge. In this work, we developed a new Monte Carlo Search (MCS) algorithm, DrugSynthMC (Drug Synthesis using Monte Carlo), in conjunction with DL and statistical-based priors to generate thousands of interpretable chemical structures and novel drug-like molecules per second. DrugSynthMC produces drug-like compounds using an atom-based search model that builds molecules as SMILES, character by character. Designed molecules follow Lipinski's "rule of 5″, show a high proportion of highly water-soluble nontoxic predicted-to-be synthesizable compounds, and efficiently expand the chemical space within the libraries, without reliance on training data sets, synthesizability metrics, or enforcing during SMILES generation. Our approach can function with or without an underlying neural network and is thus easily explainable and versatile. This ease in drug-like molecule generation allows for future integration of score functions aimed at different target- or job-oriented goals. Thus, DrugSynthMC is expected to enable the functional assessment of large compound libraries covering an extensive novel chemical space, overcoming the limitations of existing drug collections. The software is available at https://github.com/RoucairolMilo/DrugSynthMC.

Preparation, typical structural characteristics and relieving effects on osteoarthritis of squid cartilage type II collagen peptides

The promoting effects of collagen and its derivatives on bone health have been uncovered. However, the structure and effects of type II collagen peptides from squid cartilage (SCIIP) on osteoarthritis still need to be clarified. In this study, SCIIP was prepared from squid throat cartilage with pretreatment by 0.2 mol/L NaOH at a liquid-solid ratio of 10:1 for 18 h and hydrolyzation using alkaline protease and flavourzyme at 50 °C for 4 h. The structure of SCIIP was characterized as a molecular weight lower than 5 kDa (accounting for 87.7 %), a high glycine level of 35.0 %, typical FTIR and CD features of collagen peptides, and a repetitive sequence of Gly-X-Y. GP(Hyp)GPD and GPAGP(Hyp)GD were separated and identified from SCIIP, and their binding energies with TLR4/MD-2 were - 8.4 and - 8.0 kcal/mol, respectively. SCIIP effectively inhibited NO production in RAW264.7 macrophages and alleviated osteoarthritis in rats through the TLR4/NF-κB pathway. Therefore, SCIIP exhibited the potential for application as an anti-osteoarthritis supplement.

Building shape-focused pharmacophore models for effective docking screening

The performance of molecular docking can be improved by comparing the shape similarity of the flexibly sampled poses against the target proteins' inverted binding cavities. The effectiveness of these pseudo-ligands or negative image-based models in docking rescoring is boosted further by performing enrichment-driven optimization. Here, we introduce a novel shape-focused pharmacophore modeling algorithm O-LAP that generates a new class of cavity-filling models by clumping together overlapping atomic content via pairwise distance graph clustering. Top-ranked poses of flexibly docked active ligands were used as the modeling input and multiple alternative clustering settings were benchmark-tested thoroughly with five demanding drug targets using random training/test divisions. In docking rescoring, the O-LAP modeling typically improved massively on the default docking enrichment; furthermore, the results indicate that the clustered models work well in rigid docking. The C+ +/Qt5-based algorithm O-LAP is released under the GNU General Public License v3.0 via GitHub ( https://github.com/jvlehtonen/overlap-toolkit ). SCIENTIFIC CONTRIBUTION: This study introduces O-LAP, a C++/Qt5-based graph clustering software for generating new type of shape-focused pharmacophore models. In the O-LAP modeling, the target protein cavity is filled with flexibly docked active ligands, the overlapping ligand atoms are clustered, and the shape/electrostatic potential of the resulting model is compared against the flexibly sampled molecular docking poses. The O-LAP modeling is shown to ensure high enrichment in both docking rescoring and rigid docking based on comprehensive benchmark-testing.

Phytoconstituents of Artemisia Annua as potential inhibitors of SARS CoV2 main protease: an in silico study

BACKGROUND

In November 2019, the world faced a pandemic called SARS-CoV-2, which became a major threat to humans and continues to be. To overcome this, many plants were explored to find a cure.

METHODS

Therefore, this research was planned to screen out the active constituents from Artemisia annua that can work against the viral main protease Mpro as this non-structural protein is responsible for the cleavage of replicating enzymes of the virus. Twenty-five biocompounds belonging to different classes namely alpha-pinene, beta-pinene, carvone, myrtenol, quinic acid, caffeic acid, quercetin, rutin, apigenin, chrysoplenetin, arteannunin b, artemisinin, scopoletin, scoparone, artemisinic acid, deoxyartemisnin, artemetin, casticin, sitogluside, beta-sitosterol, dihydroartemisinin, scopolin, artemether, artemotil, artesunate were selected. Virtual screening of these ligands was carried out against drug target Mpro by CB dock.

RESULTS

Quercetin, rutin, casticin, chrysoplenetin, apigenin, artemetin, artesunate, sopolin and sito-gluside were found as hit compounds. Further, ADMET screening was conducted which represented Chrysoplenetin as a lead compound. Azithromycin was used as a standard drug. The interactions were studied by PyMol and visualized in LigPlot. Furthermore, the RMSD graph shows fluctuations at various points at the start of simulation in Top1 (Azithromycin) complex system due to structural changes in the helix-coil-helix and beta-turn-beta changes at specific points resulting in increased RMSD with a time frame of 50 ns. But this change remains stable after the extension of simulation time intervals till 100 ns. On other side, the Top2 complex system remains highly stable throughout the time scale. No such structural dynamics were observed bu the ligand attached to the active site residues binds strongly.

CONCLUSION

This study facilitates researchers to develop and discover more effective and specific therapeutic agents against SARS-CoV-2 and other viral infections. Finally, chrysoplenetin was identified as a more potent drug candidate to act against the viral main protease, which in the future can be helpful.

Deciphering the Lexicon of Protein Targets: A Review on Multifaceted Drug Discovery in the Era of Artificial Intelligence

Understanding protein sequence and structure is essential for understanding protein-protein interactions (PPIs), which are essential for many biological processes and diseases. Targeting protein binding hot spots, which regulate signaling and growth, with rational drug design is promising. Rational drug design uses structural data and computational tools to study protein binding sites and protein interfaces to design inhibitors that can change these interactions, thereby potentially leading to therapeutic approaches. Artificial intelligence (AI), such as machine learning (ML) and deep learning (DL), has advanced drug discovery and design by providing computational resources and methods. Quantum chemistry is essential for drug reactivity, toxicology, drug screening, and quantitative structure-activity relationship (QSAR) properties. This review discusses the methodologies and challenges of identifying and characterizing hot spots and binding sites. It also explores the strategies and applications of artificial-intelligence-based rational drug design technologies that target proteins and protein-protein interaction (PPI) binding hot spots. It provides valuable insights for drug design with therapeutic implications. We have also demonstrated the pathological conditions of heat shock protein 27 (HSP27) and matrix metallopoproteinases (MMP2 and MMP9) and designed inhibitors of these proteins using the drug discovery paradigm in a case study on the discovery of drug molecules for cancer treatment. Additionally, the implications of benzothiazole derivatives for anticancer drug design and discovery are deliberated.

GSScore: a novel Graphormer-based shell-like scoring method for protein-ligand docking

Protein-ligand interactions (PLIs) are essential for cellular activities and drug discovery. But due to the complexity and high cost of experimental methods, there is a great demand for computational approaches to recognize PLI patterns, such as protein-ligand docking. In recent years, more and more models based on machine learning have been developed to directly predict the root mean square deviation (RMSD) of a ligand docking pose with reference to its native binding pose. However, new scoring methods are pressingly needed in methodology for more accurate RMSD prediction. We present a new deep learning-based scoring method for RMSD prediction of protein-ligand docking poses based on a Graphormer method and Shell-like graph architecture, named GSScore. To recognize near-native conformations from a set of poses, GSScore takes atoms as nodes and then establishes the docking interface of protein-ligand into multiple bipartite graphs within different shell ranges. Benefiting from the Graphormer and Shell-like graph architecture, GSScore can effectively capture the subtle differences between energetically favorable near-native conformations and unfavorable non-native poses without extra information. GSScore was extensively evaluated on diverse test sets including a subset of PDBBind version 2019, CASF2016 as well as DUD-E, and obtained significant improvements over existing methods in terms of RMSE, $R$ (Pearson correlation coefficient), Spearman correlation coefficient and Docking power.

Large-Scale Pretraining Improves Sample Efficiency of Active Learning-Based Virtual Screening

Virtual screening of large compound libraries to identify potential hit candidates is one of the earliest steps in drug discovery. As the size of commercially available compound collections grows exponentially to the scale of billions, active learning and Bayesian optimization have recently been proven as effective methods of narrowing down the search space. An essential component of those methods is a surrogate machine learning model that predicts the desired properties of compounds. An accurate model can achieve high sample efficiency by finding hits with only a fraction of the entire library being virtually screened. In this study, we examined the performance of a pretrained transformer-based language model and graph neural network in a Bayesian optimization active learning framework. The best pretrained model identifies 58.97% of the top-50,000 compounds after screening only 0.6% of an ultralarge library containing 99.5 million compounds, improving 8% over the previous state-of-the-art baseline. Through extensive benchmarks, we show that the superior performance of pretrained models persists in both structure-based and ligand-based drug discovery. Pretrained models can serve as a boost to the accuracy and sample efficiency of active learning-based virtual screening.

Applying molecular docking to pesticides

Pesticide creation is related to the development of sustainable agricultural and ecological safety, and molecular docking technology can effectively help in pesticide innovation. This paper introduces the basic theory behind molecular docking, pesticide databases, and docking software. It also summarizes the application of molecular docking in the pesticide field, including the virtual screening of lead compounds, detection of pesticides and their metabolites in the environment, reverse screening of pesticide targets, and the study of resistance mechanisms. Finally, problems with the use of molecular docking technology in pesticide creation are discussed, and prospects for the future use of molecular docking technology in new pesticide development are discussed. © 2023 Society of Chemical Industry.

ViTScore: A Novel Three-Dimensional Vision Transformer Method for Accurate Prediction of Protein-Ligand Docking Poses

Protein-ligand interactions (PLIs) are essential for cellular activities and drug discovery, and due to the complexity and high cost of experimental methods, there is a great demand for computational approaches, such as protein-ligand docking, to decipher PLI patterns. One of the most challenging aspects of protein-ligand docking is to identify near-native conformations from a set of poses, but traditional scoring functions still have limited accuracy. Therefore, new scoring methods are urgently needed for methodological and/or practical implications. We present a novel deep learning-based scoring function for ranking protein-ligand docking poses based on Vision Transformer (ViT), named ViTScore. To recognize near-native poses from a set of poses, ViTScore voxelizes the protein-ligand interactional pocket into a 3D grid labeled by the occupancy contribution of atoms in different physicochemical classes. This allows ViTScore to capture the subtle differences between spatially and energetically favorable near-native poses and unfavorable non-native poses without needing extra information. After that, ViTScore will output the prediction of the root mean square deviation (rmsd) of a docking pose with reference to the native binding pose. ViTScore is extensively evaluated on diverse test sets including PDBbind2019 and CASF2016, and obtains significant improvements over existing methods in terms of RMSE, R and docking power. Moreover, the results demonstrate that ViTScore is a promising scoring function for protein-ligand docking, and it can be used to accurately identify near-native poses from a set of poses. Furthermore, the results suggest that ViTScore is a powerful tool for protein-ligand docking, and it can be used to accurately identify near-native poses from a set of poses. Additionally, ViTScore can be used to identify potential drug targets and to design new drugs with improved efficacy and safety.

Machine Learning-Boosted Docking Enables the Efficient Structure-Based Virtual Screening of Giga-Scale Enumerated Chemical Libraries

The emergence of ultra-large screening libraries, filled to the brim with billions of readily available compounds, poses a growing challenge for docking-based virtual screening. Machine learning (ML)-boosted strategies like the tool HASTEN combine rapid ML prediction with the brute-force docking of small fractions of such libraries to increase screening throughput and take on giga-scale libraries. In our case study of an anti-bacterial chaperone and an anti-viral kinase, we first generated a brute-force docking baseline for 1.56 billion compounds in the Enamine REAL lead-like library with the fast Glide high-throughput virtual screening protocol. With HASTEN, we observed robust recall of 90% of the true 1000 top-scoring virtual hits in both targets when docking only 1% of the entire library. This reduction of the required docking experiments by 99% significantly shortens the screening time. In the kinase target, the employment of a hydrogen bonding constraint resulted in a major proportion of unsuccessful docking attempts and hampered ML predictions. We demonstrate the optimization potential in the treatment of failed compounds when performing ML-boosted screening and benchmark and showcase HASTEN as a fast and robust tool in a growing arsenal of approaches to unlock the chemical space covered by giga-scale screening libraries for everyday drug discovery campaigns.

Construction and Catalytic Study of Affinity Peptide Orientation and Light Crosslinking Immobilized Sucrose Isomerase

A novel affinity peptide orientation and light-controlled covalent immobilized method was developed. Sucrose isomerase (SI) was selected as the model enzyme. Molecular simulation was first performed to select the targeted immobilization region. Subsequently, a short peptide (H2N-VNIGGX-COOH, VG) with high affinity to this region was rationally designed. Thereafter, 4-benzoyl-l-phenylalanine with the photosensitive group of benzophenone was introduced. Then, the affinity between the ligand and the SI was validated using molecular dynamics simulation. Thereafter, the SI was directionally immobilized onto the surface of the epoxy resin (EP) guided by VG via photo-crosslinking, and thus the oriented photo-crosslinking enzymes were obtained. The enzymatic activity, thermostability, and reusability of the affinity directional photo-crosslinked immobilized sucrose isomerase (hv-EP-VG-SI) were systematically studied. The oriented immobilization enzymes were significantly improved in recycling and heat resistance. Moreover, hv-EP-VG-SI retained more than 90% of the original activity and 50% of the activity after 11 cycles.

An Updated Review on Developing Small Molecule Kinase Inhibitors Using Computer-Aided Drug Design Approaches

Small molecule kinase inhibitors (SMKIs) are of heightened interest in the field of drug research and development. There are 79 (as of July 2023) small molecule kinase inhibitors that have been approved by the FDA and hundreds of kinase inhibitor candidates in clinical trials that have shed light on the treatment of some major diseases. As an important strategy in drug design, computer-aided drug design (CADD) plays an indispensable role in the discovery of SMKIs. CADD methods such as docking, molecular dynamic, quantum mechanics/molecular mechanics, pharmacophore, virtual screening, and quantitative structure-activity relationship have been applied to the design and optimization of small molecule kinase inhibitors. In this review, we provide an overview of recent advances in CADD and SMKIs and the application of CADD in the discovery of SMKIs.

Feasibility study on the use of "Qi-tonifying medicine compound" as an anti-fatigue functional food ingredient based on network pharmacology and molecular docking

Introduction

Fatigue has attracted broad attention in recent years due to its high morbidity rates. The use of functional foods to relieve fatigue-associated symptoms is becoming increasingly popular and has achieved relatively good results. In this study, network pharmacology and molecular docking strategies were used to establish the material basis and mechanisms of Chinese herbal compounds in fatigue treatment. According to traditional medicine theories and relevant guidance documents published by the Chinese Ministry of Health, four herbal medicines, including Eucommia ulmoides Oliver bark, Eucommia ulmoides Oliver male flower, Panax notoginseng, and Syzygium aromaticum (EEPS), were selected to constitute the anti-fatigue herbal compound that may be suitable as functional food ingredients.

Methods

The major active ingredients in EEPS were identified via comprehensive literature search and Traditional Chinese Medicine Systems Pharmacology database search. Corresponding targets for these ingredients were predicted using SwissTargetPrediction. The network was constructed using Cytoscape 3.9.1 to obtain key ingredients. Prediction of absorption, distribution, metabolism, excretion and toxicity properties was performed using the ADMETIab 2.0 database. The anti-fatigue targets were retrieved from GeneCards v5.13, OMIM, TTD and DisGeNET 7.0 databases. Then, the potential targets of EEPS in fatigue treatment were screened through a Venn diagram. A protein-protein interaction (PPI) network of these overlapping targets was constructed, and the hub targets in the network selected through topological screening. Gene Ontology and KEGG pathway enrichment analyses were performed using the DAVID database and the bioinformatics online platform. Finally, AutoDock tools were used to verify the binding capacity between the key active ingredients and the core targets.

Results and Discussion

This study identified the active ingredients and potential molecular mechanisms of EEPS in fatigue treatment, which will provide a foundation for future research on applications of herbal medicines in the functional food industry.

BEAR: A Novel Virtual Screening Method Based on Large-Scale Bioactivity Data

Data-driven drug discovery exploits a comprehensive set of big data to provide an efficient path for the development of new drugs. Currently, publicly available bioassay data sets provide extensive information regarding the bioactivity profiles of millions of compounds. Using these large-scale drug screening data sets, we developed a novel in silico method to virtually screen hit compounds against protein targets, named BEAR (Bioactive compound Enrichment by Assay Repositioning). The underlying idea of BEAR is to reuse bioassay data for predicting hit compounds for targets other than their originally intended purposes, i.e., "assay repositioning". The BEAR approach differs from conventional virtual screening methods in that (1) it relies solely on bioactivity data and requires no physicochemical features of either the target or ligand. (2) Accordingly, structurally diverse candidates are predicted, allowing for scaffold hopping. (3) BEAR shows stable performance across diverse target classes, suggesting its general applicability. Large-scale cross-validation of more than a thousand targets showed that BEAR accurately predicted known ligands (median area under the curve = 0.87), proving that BEAR maintained a robust performance even in the validation set with additional constraints. In addition, a comparative analysis demonstrated that BEAR outperformed other machine learning models, including a recent deep learning model for ABC transporter family targets. We predicted P-gp and BCRP dual inhibitors using the BEAR approach and validated the predicted candidates using in vitro assays. The intracellular accumulation effects of mitoxantrone, a well-known P-gp/BCRP dual substrate for cancer treatment, confirmed nine out of 72 dual inhibitor candidates preselected by primary cytotoxicity screening. Consequently, these nine hits are novel and potent dual inhibitors for both P-gp and BCRP, solely predicted by bioactivity profiles without relying on any structural information of targets or ligands.

Application of Molecular Simulation Methods in Food Science: Status and Prospects

Molecular simulation methods, such as molecular docking, molecular dynamic (MD) simulation, and quantum chemical (QC) calculation, have become popular as characterization and/or virtual screening tools because they can visually display interaction details that in vitro experiments can not capture and quickly screen bioactive compounds from large databases with millions of molecules. Currently, interdisciplinary research has expanded molecular simulation technology from computer aided drug design (CADD) to food science. More food scientists are supporting their hypotheses/results with this technology. To understand better the use of molecular simulation methods, it is necessary to systematically summarize the latest applications and usage trends of molecular simulation methods in the research field of food science. However, this type of review article is rare. To bridge this gap, we have comprehensively summarized the principle, combination usage, and application of molecular simulation methods in food science. We also analyzed the limitations and future trends and offered valuable strategies with the latest technologies to help food scientists use molecular simulation methods.

Understanding the biodegradation pathways of azo dyes by immobilized white-rot fungus, Trametes hirsuta D7, using UPLC-PDA-FTICR MS supported by in silico simulations and toxicity assessment

No biodegradation methods are absolute in the treatment of all textile dyes, which leads to structure-dependent degradation. In this study, biodegradation of three azo dyes, reactive black 5 (RB5), acid blue 113 (AB113), and acid orange 7 (AO7), was investigated using an immobilized fungus, Trametes hirsuta D7. The degraded metabolites were identified using UPLC-PDA-FTICR MS and the biodegradation pathway followed was proposed. RB5 (92%) and AB113 (97%) were effectively degraded, whereas only 30% of AO7 was degraded. Molecular docking simulations were performed to determine the reason behind the poor degradation of AO7. Weak binding affinity, deficiency in H-bonding interactions, and the absence of interactions between the azo (-NN-) group and active residues of the model laccase enzyme were responsible for the low degradation efficiency of AO7. Furthermore, cytotoxicity and genotoxicity assays confirmed that the fungus-treated dye produced non-toxic metabolites. The observations of this study will be useful for understanding and further improving enzymatic dye biodegradation.

Automated and Efficient Generation of General Molecular Aggregate Structures

Modeling intermolecular interactions of complex non-covalent structures is important in many areas of chemistry. To facilitate the generation of reasonable dimer, oligomer, and general aggregate geometries, we introduce an automated computational interaction site screening (aISS) workflow. This easy-to-use tool combines a genetic algorithm employing the intermolecular force-field xTB-IFF for initial search steps with the general force-field GFN-FF and the semi-empirical GFN2-xTB method for geometry optimizations. Compared with the alternative CREST program, aISS yields similar results but with computer time savings of 1-3 orders of magnitude. This allows for the treatment of systems with thousands of atoms composed of elements up to radon, e.g., metal-organic complexes, or even polyhedra and zeolite cut-outs which were not accessible before. Moreover, aISS can identify reactive sites and provides options like site-directed (user-guided) screening.

Bionics design of affinity peptide inhibitors for SARS-CoV-2 RBD to block SARS-CoV-2 RBD-ACE2 interactions

Coronavirus Disease 2019 (COVID-19), has already posed serious threats and impacts on the health of the population and the country's economy. Therefore, it is of great theoretical significance and practical application value to better understand the process of COVID-19 infection and develop effective therapeutic drugs. It is known that the receptor-binding structural domain (SARS-CoV-2 RBD) on the spike protein of the novel coronavirus directly mediates its interaction with the host receptor angiotensin-converting enzyme 2 (ACE2), and thus blocking SARS-CoV-2 RBD-ACE2 interaction is capable of inhibiting SARS-CoV-2 infection. Firstly, the interaction mechanism between SARS-CoV-2RBD-ACE2 was explored using molecular dynamics simulation (MD) coupled with molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) free energy calculation method. The results of energy analysis showed that the key residues R403, R408, K417, and Y505 of SARS-CoV-2 RBD and the key residues D30, E37, D38, and Y41 of ACE2 were identified. Therefore, according to the hotspot residues of ACE2 and their distribution, a short peptide library of high-affinity SARS-CoV-2 RBD was constructed. And by using molecular docking virtual screening, six short peptides including DDFEDY, DEFEDY, DEYEDY, DFVEDY, DFHEDY, and DSFEDY with high affinity for SARS-CoV-2 RBD were identified. The results of MD simulation further confirmed that DDFEDY, DEYEDY, and DFVEDY are expected to be effective inhibitors. Finally, the allergenicity, toxicity and solubility properties of the three peptide inhibitors were validated.

Naphthoquinones biflorin and bis-biflorin (Capraria biflora) as possible inhibitors of the fungus Candida auris polymerase: molecular docking, molecular dynamics, MM/GBSA calculations and in silico drug-likeness study

A new worldwide concern has emerged with the recent emergence of infections caused by Candida auris. This reflects its comparative ease of transmission, substantial mortality, and the increasing level of resistance seen in the three major classes of antifungal drugs. Efforts to create a better design for structure-based drugs that described numerous modifications and the search for secondary metabolic structures derived from plant species are likely to reduce the virulence of several fungal pathogens. In this context, the present work aimed to evaluate in silico two naphthoquinones isolated from the roots of Capraria biflora, biflorin, and its dimmer, bis-biflorin, as potential inhibitors of Candida auris polymerase. Based on the simulation performed with the two naphthoquinones, biflorin and bis-biflorin, it can be stated that bis-biflorin showed the best interactions with Candida auris polymerase. Still, biflorin also demonstrated favorable coupling energy. Predictive pharmacokinetic assays suggest that biflorin has high oral bioavailability and more excellent metabolic stability compared to the bis-biflorin analogue. constituting a promising pharmacological tool.Communicated by Ramaswamy H. Sarma.

Computational insights for predicting the binding and selectivity of peptidomimetic plasmepsin IV inhibitors against cathepsin D

Plasmepsins (Plms) are aspartic proteases involved in the degradation of human hemoglobin by P. falciparum and are essential for the survival and growth of the parasite. Therefore, Plm enzymes are reported as an important antimalarial drug target. Herein, we have applied molecular docking, molecular dynamics (MD) simulations, and binding free energy with the Linear Interaction Energy (LIE) approach to investigate the binding of peptidomimetic PlmIV inhibitors with a particular focus on understanding their selectivity against the human Asp protease cathepsin D (CatD). The residual decomposition analysis results suggest that amino acid differences in the subsite S3 of PlmIV and CatD are responsible for the higher selectivity of the 5a inhibitor. These findings yield excellent agreement with experimental binding data and provide new details regarding van der Waals and electrostatic interactions of subsite residues as well as structural properties of the PlmIV and CatD systems.

Compounds in Indonesian Ginger Rhizome Extracts and Their Potential for Anti-Skin Aging Based on Molecular Docking

Skin aging is a condition caused by reactive oxygen species (ROS) and advanced glycation end products (AGEs). Indonesian gingers (Zingiber officinale), which consists of Gajah (GG), Red (MM), and Emprit (EE) ginger, are thought to produce anti-skin aging compounds through enzyme inhibition. The enzymes used in the molecular docking study were collagenase, hyaluronidase, elastase, and tyrosinase. This study aimed to determine the compounds contained in Indonesian ginger rhizome ethanolic extracts using liquid chromatography–mass spectrometry/mass spectrometry to differentiate metabolites contained in the different Indonesian ginger rhizome extracts. A principal component analysis (PCA) and a heat map analysis were used in order to determine which compounds and extracts contained potential anti-skin aging properties based on a molecular docking study. Ascorbic acid was used as a control ligand in the molecular docking study. Ninety-eight compounds were identified in three different ginger rhizomes extracts and were grouped into three separate quadrants. The most potent compound for anti-skin aging in the Indonesian ginger rhizome extracts was octinoxate. Octinoxate showed a high abundance in the EE ginger rhizome extract. Therefore, the EE ginger extract was the Indonesian ginger rhizome extract with the greatest potential for anti-skin aging.

Molecular docking approaches and its significance in assessing the antioxidant properties in different compounds

In the last few years, the significance of antioxidant compounds and their properties has attracted great interest from the scientific community. The role of an antioxidant in managing & regulating oxidative stress and also in the protection of the human body from severe adverse effects due to excess release of free radicles or reactive oxygen species (ROS) is remarkable. From aiding protection & combating severe illnesses such as cancer, neurodegeneration, aging, and diabetes to being a vital part of the treatment of SARs-CoV-19 is of great importance. Therefore, the study of anti-oxidants is of great importance in human sustenance. Additionally, molecular docking techniques and their various mathematical features help in understanding the molecular interactions of anti-oxidants based on their lowest binding energy. The evaluation of the binding score between two constituent molecules will provide insight as to the binding process and also suggest possible novel therapeutic targets for the treatment of diseases. In this chapter, we will discuss the significance of molecular docking techniques in the study of antioxidant compounds.

New Chemicals Suppressing SARS-CoV-2 Replication in Cell Culture

Candidates to being inhibitors of the main protease (Mpro) of SARS-CoV-2 were selected from the database of Voronezh State University using molecular modeling. The database contained approximately 19,000 compounds represented by more than 41,000 ligand conformers. These ligands were docked into Mpro using the SOL docking program. For one thousand ligands with best values of the SOL score, the protein–ligand binding enthalpy was calculated by the PM7 quantum-chemical method with the COSMO solvent model. Using the SOL score and the calculated protein–ligand binding enthalpies, eighteen compounds were selected for the experiments. Several of these inhibitors suppressed the replication of the coronavirus in cell culture, and we used the best three among them in the search for chemical analogs. Selection among analogs using the same procedure followed by experiments led to identification of seven inhibitors of the SARS-CoV-2 replication in cell culture with EC50 values at the micromolar level. The identified inhibitors belong to three chemical classes. The three inhibitors, 4,4-dimethyldithioquinoline derivatives, inhibit SARS-CoV-2 replication in Vero E6 cell culture just as effectively as the best published non-covalent inhibitors, and show low cytotoxicity. These results open up a possibility to develop antiviral drugs against the SARS-CoV-2 coronavirus.

An Effective Swarm Intelligence Optimization Algorithm for Flexible Ligand Docking

In general, flexible ligand docking is used for docking simulations under the premise that the position of the binding site is already known, and meanwhile it can also be used without prior knowledge of the binding site. However, most of the optimization search algorithms used in popular docking software are far from being ideal in the first case, and they can hardly be directly utilized for the latter case due to the relatively large search area. In order to design an algorithm that can flexibly adapt to different sizes of the search area, we propose an effective swarm intelligence optimization algorithm in this paper, called diversity-controlled Lamarckian quantum particle swarm optimization (DCL-QPSO). The highlights of the algorithm are a diversity-controlled strategy and a modified local search method. Integrated with the docking environment of Autodock, the DCL-QPSO is compared with Autodock Vina, Glide and other two Autodock-based search algorithms for flexible ligand docking. Experimental results revealed that the proposed algorithm has a performance comparable to those of Autodock Vina and Glide for dockings within a certain area around the binding sites, and is a more effective solver than all the compared methods for dockings without prior knowledge of the binding sites.

Synthesis, Characterization and Bioactivity Evaluation of a Novel Nano Bagasse Xylan/Andrographolide Grafted and Esterified Derivative

In the in-depth research that has been conducted on nanometer biomaterials, how to use the biomass resources with high activity and low toxicity to prepare nanomaterials for biomedical applications has attracted much attention. To realize efficient and comprehensive utilization of biomass, bagasse xylan/andrographolide (BX/AD) was ued as a raw material and glycyrrhetinic acid (GA) as an esterification agent to synthesize bagasse xylan/andrographolide esterified derivative (GA-BX/AD). Then, the bagasse xylan/andrographolide grafted and esterified derivative (GA-BX/AD-g-IA) was synthesized by the graft crosslinking reactions using itaconic acid (IA) as graft monomer. The better synthesis conditions were optimized by single factor experiments, the degree of esterification substitution (DS) was 0.43, and the grafting rate (G) of the product reached 42%. The structure and properties of the product were characterized by FTIR, XRD, DTG, SEM, and ¹H NMR. The results showed that the product morphology was significantly changed, and the nanoparticles were spherical with a particle size of about 100 nm. The anti-cancer activity of the product was measured. The molecular docking simulations revealed that the product had good docking activity with human glucocorticoid protein (6CFN) with a binding free energy of 14.38 kcal/mol. The MTT assay showed that the product had a strong inhibitory effect on the growth of human liver cancer cells (BEL-7407) and gastric cancer cells (MGC80-3), with inhibition ratio of 38.41 ± 5.32% and 32.69 ± 4.87%. Therefore, this nanomaterial is expected to be applied to the development and utilization of drug carriers and functional materials.

Emerging Promise of Computational Techniques in Anti-Cancer Research: At a Glance

Research on the immune system and cancer has led to the development of new medicines that enable the former to attack cancer cells. Drugs that specifically target and destroy cancer cells are on the horizon; there are also drugs that use specific signals to stop cancer cells multiplying. Machine learning algorithms can significantly support and increase the rate of research on complicated diseases to help find new remedies. One area of medical study that could greatly benefit from machine learning algorithms is the exploration of cancer genomes and the discovery of the best treatment protocols for different subtypes of the disease. However, developing a new drug is time-consuming, complicated, dangerous, and costly. Traditional drug production can take up to 15 years, costing over USD 1 billion. Therefore, computer-aided drug design (CADD) has emerged as a powerful and promising technology to develop quicker, cheaper, and more efficient designs. Many new technologies and methods have been introduced to enhance drug development productivity and analytical methodologies, and they have become a crucial part of many drug discovery programs; many scanning programs, for example, use ligand screening and structural virtual screening techniques from hit detection to optimization. In this review, we examined various types of computational methods focusing on anticancer drugs. Machine-based learning in basic and translational cancer research that could reach new levels of personalized medicine marked by speedy and advanced data analysis is still beyond reach. Ending cancer as we know it means ensuring that every patient has access to safe and effective therapies. Recent developments in computational drug discovery technologies have had a large and remarkable impact on the design of anticancer drugs and have also yielded useful insights into the field of cancer therapy. With an emphasis on anticancer medications, we covered the various components of computer-aided drug development in this paper. Transcriptomics, toxicogenomics, functional genomics, and biological networks are only a few examples of the bioinformatics techniques used to forecast anticancer medications and treatment combinations based on multi-omics data. We believe that a general review of the databases that are now available and the computational techniques used today will be beneficial for the creation of new cancer treatment approaches.

The role of cholesterol binding in the control of cholesterol by the Scap-Insig system

Scap and Insig, two proteins embedded in the membrane of the endoplasmic reticulum (ER), regulate the synthesis of cholesterol in animal cells by forming a dimer in the presence of high concentrations of cholesterol. Cryo-electron microscopic structures for the Scap-Insig dimer show a sterol-binding site at the dimer interface, but none of the structures include cholesterol itself. Here, a molecular docking approach developed to characterise cholesterol binding to the transmembrane (TM) regions of membrane proteins is used to characterise cholesterol binding to sites on the TM surface of the dimer and to the interfacial binding site. Binding of cholesterol is also observed at sites on the extra-membranous luminal domains of Scap, but the properties of these sites suggest that they will be unoccupied in vivo. Comparing the structure of Scap in the dimer with that predicted by AlphaFold for monomeric Scap suggests that dimer formation could result in relocation of TM helix 7 of Scap and of the loop between TM6 and 7, and that this could be the key change on Scap that signals that there is a high concentration of cholesterol in the ER.

From Traditional Ethnopharmacology to Modern Natural Drug Discovery: A Methodology Discussion and Specific Examples

Ethnopharmacology, through the description of the beneficial effects of plants, has provided an early framework for the therapeutic use of natural compounds. Natural products, either in their native form or after crude extraction of their active ingredients, have long been used by different populations and explored as invaluable sources for drug design. The transition from traditional ethnopharmacology to drug discovery has followed a straightforward path, assisted by the evolution of isolation and characterization methods, the increase in computational power, and the development of specific chemoinformatic methods. The deriving extensive exploitation of the natural product chemical space has led to the discovery of novel compounds with pharmaceutical properties, although this was not followed by an analogous increase in novel drugs. In this work, we discuss the evolution of ideas and methods, from traditional ethnopharmacology to in silico drug discovery, applied to natural products. We point out that, in the past, the starting point was the plant itself, identified by sustained ethnopharmacological research, with the active compound deriving after extensive analysis and testing. In contrast, in recent years, the active substance has been pinpointed by computational methods (in silico docking and molecular dynamics, network pharmacology), followed by the identification of the plant(s) containing the active ingredient, identified by existing or putative ethnopharmacological information. We further stress the potential pitfalls of recent in silico methods and discuss the absolute need for in vitro and in vivo validation as an absolute requirement. Finally, we present our contribution to natural products' drug discovery by discussing specific examples, applying the whole continuum of this rapidly evolving field. In detail, we report the isolation of novel antiviral compounds, based on natural products active against influenza and SARS-CoV-2 and novel substances active on a specific GPCR, OXER1.

Novel small synthetic HIV-1 V3 crown variants: CCR5 targeting ligands

The CC chemokine receptor 5 (CCR5) antagonism represents a promising pharmacological strategy for therapeutic intervention as it plays a significant role in reducing the severity and progression of a wide range of pathological conditions. Here we designed and generated peptide ligands targeting the chemokine receptor, CCR5, that were derived from the critical interaction sites of the V3 crown domain of envelope protein glycoprotein gp120 (TRKSIHIGPGRAFYTTGEI) of HIV-1 using computational biology approach and the peptide sequence corresponding to this region was taken as the template peptide, designated as TMP-1. The peptide variants were synthesized by employing Fmoc chemistry using polymer support and were labelled with rhodamine B to study their interaction with the CCR5 receptor expressed on various cells. TMP-1 and TMP-2 were selected as the high-affinity ligands from in vitro receptor-binding assays. Specific receptor-binding experiments in activated peripheral blood mononuclear cells and HOS.CCR5 cells indicated that TMP-1 and TMP-2 had significant CCR5 specificity. Further, the functional analysis of TMP peptides using chemotactic migration assay showed that both peptides did not mediate the migration of responsive cells. Thus, template TMP-1 and TMP-2 represent promising CCR5 targeting peptide candidates.

Zerumbone, a humulane sesquiterpene from Syringa pinnatifolia, attenuates cardiac fibrosis by inhibiting of the TGF-β1/Smad signaling pathway after myocardial infarction in mice

BACKGROUND

Zerumbone (ZER) is a humulane sesquiterpene isolated from Syringa pinnatifolia Hemsl., a representative Mongolian herbal medicine that is used to treat cardiovascular diseases. Cardiac fibrosis is a common pathological process in cardiovascular disease that results from the excessive accumulation of extracellular matrix (ECM), and the transforming growth factor (TGF)-β/Smad pathway is a canonical signaling pathway that directly induces expressions of ECM-related genes. Currently, the cardioprotective effect and underlying mechanisms of ZER on the inhibition of cardiac fibrosis are not well known.

PURPOSE

To explore the cardioprotective properties and pharmacological mechanism of ZER against cardiac fibrosis via the TGF-β1/Smad signaling pathway.

METHODS

Myocardial infarction (MI) model was induced by ligation of the left anterior descending coronary artery in ICR mice. The mice were randomly divided into six groups: sham, model, low-dose ZER (ZER-L), medium-dose ZER (ZER-M), high-dose ZER (ZER-H) and fosinopril. Mice in each group were intragastrically administered treatments for 21 days, and cardiac function was evaluated by 2D echocardiography. The pathological structure of the heart was examined by hematoxylin and eosin (HE) and Masson staining. Content of collagen I and collagen III were assessed by immunofluorescence methods. The inhibitory effect of ZER on TGF-β1 protein expression was predicted by molecular docking technology. Reverse transcriptase polymerase chain reaction (RT-PCR) and western blotting were used to measure the levels of genes and proteins expressed in the TGF-β1/Smad signaling pathway and MMPs. TGF-β1-treated cardiac fibroblasts (CFs) of neonatal SD rats were adopted for in vitro studies.

RESULTS

Cardiac ejection fraction (EF) and fractional shortening (FS) in the model group were markedly decreased compared with those in the sham group, indicating that the MI model was successfully established. ZER and fosinopril elevated EF and FS values, suggesting cardioprotective effects. Pathological staining and immunofluorescence analysis showed that the content of collagen I and collagen III increased in the cardiac tissue of mice in model group, while ZER treatment obviously reduced collagen levels. The molecular docking simulations predicted the hydrophobic interactions between ZER and TGF-β1. In addition, the expression of TGF-β1, p-Smad2/3 and MMPs in the ZER treatment group was significantly decreased compared with the model group. In vitro studies further confirmed that α-smooth muscle actin (α-SMA) and p-Smad2/3 increased markedly in cardiac fibroblasts after incubation with TGF-β1, and treatment with ZER suppressed the expression of α-SMA and TGF-β1 downstream proteins in cardiac fibroblasts.

CONCLUSION

ZER rescues cardiac function by attenuating cardiac fibrosis, and the antifibrotic effect may be mediated by blocking the TGF-β1/Smad pathway.

Insights into the Pharmacological Effects of Flavonoids: The Systematic Review of Computer Modeling

Computer modeling is a method that is widely used in scientific investigations to predict the biological activity, toxicity, pharmacokinetics, and synthesis strategy of compounds based on the structure of the molecule. This work is a systematic review of articles performed in accordance with the recommendations of PRISMA and contains information on computer modeling of the interaction of classical flavonoids with different biological targets. The review of used computational approaches is presented. Furthermore, the affinities of flavonoids to different targets that are associated with the infection, cardiovascular, and oncological diseases are discussed. Additionally, the methodology of bias risks in molecular docking research based on principles of evidentiary medicine was suggested and discussed. Based on this data, the most active groups of flavonoids and lead compounds for different targets were determined. It was concluded that flavonoids are a promising object for drug development and further research of pharmacology by in vitro, ex vivo, and in vivo models is required.

Development of an Automatic Pipeline for Participation in the CELPP Challenge

The prediction of how a ligand binds to its target is an essential step for Structure-Based Drug Design (SBDD) methods. Molecular docking is a standard tool to predict the binding mode of a ligand to its macromolecular receptor and to quantify their mutual complementarity, with multiple applications in drug design. However, docking programs do not always find correct solutions, either because they are not sampled or due to inaccuracies in the scoring functions. Quantifying the docking performance in real scenarios is essential to understanding their limitations, managing expectations and guiding future developments. Here, we present a fully automated pipeline for pose prediction validated by participating in the Continuous Evaluation of Ligand Pose Prediction (CELPP) Challenge. Acknowledging the intrinsic limitations of the docking method, we devised a strategy to automatically mine and exploit pre-existing data, defining—whenever possible—empirical restraints to guide the docking process. We prove that the pipeline is able to generate predictions for most of the proposed targets as well as obtain poses with low RMSD values when compared to the crystal structure. All things considered, our pipeline highlights some major challenges in the automatic prediction of protein–ligand complexes, which will be addressed in future versions of the pipeline.

Computational Analysis and Biological Activities of Oxyresveratrol Analogues, the Putative Cyclooxygenase-2 Inhibitors

Polyphenols are a large family of naturally occurring phytochemicals. Herein, oxyresveratrol was isolated from ethanolic crude extracts of Artocarpus lacucha Buch.-Ham., and chemically modified to derive its lipophilic analogues. Biological screening assays showed their inhibitory potency against cyclooxygenase-2 (COX-2) with very low cytotoxicity to the MRC-5 normal cell lines. At the catalytic site of COX-2, docking protocols with ChemPLP, GoldScore and AutoDock scoring functions were carried out to reveal hydrogen bonding interactions with key polar contacts and hydrophobic pi-interactions. For more accurate binding energetics, COX-2/ligand complexes at the binding region were computed in vacuo and implicit aqueous solvation using M06-2X density functional with 6-31G+(d,p) basis set. Our computational results confirmed that dihydrooxyresveratrol (4) is the putative inhibitor of human COX-2 with the highest inhibitory activity (IC₅₀ of 11.50 ± 1.54 µM) among studied non-fluorinated analogues for further lead optimization. Selective substitution of fluorine provides a stronger binding affinity; however, lowering the cytotoxicity of a fluorinated analogue to a normal cell is challenging. The consensus among biological activities, ChemPLP docking score and the binding energies computed at the quantum mechanical level is obviously helpful for identification of oxyresveratrol analogues as a putative anti-inflammatory agent.

Molecular structure determination, spectroscopic, quantum computational studies and molecular docking of 4-(E)-[2-(benzylamino)phenylimino) methyl-2]ethoxy phenol

A Schiff base compound 4-(E)-[2-(benzylamino)phenylimino)methyl-2]ethoxy phenol (4BPM2EP) was synthesized and spectroscopic characterization was performed using experimental methods such as FT-IR, FT-Raman and UV-Vis spectroscopy. Density functional theory (DFT/B3LYP/6-311++G(d,p)) computation was used to investigate the optimized molecular geometry, harmonic vibrational wavenumber, NMR chemical shifts, natural bond orbital (NBO) analysis, non-linear optical (NLO) properties, molecular electrostatic potential (MEP) map and Mulliken atomic charges of 4BPM2EP molecule. TD-DFT calculations have been carried out on the optimized geometry at gaseous phase, DMSO and ethanol to further understand the electronic transitions and solvents effect on the UV-Vis spectra of the compound. The assignments of vibrational modes were performed on the basis of total energy distribution (TED) using VEDA 4 program and were compared with experimental data. Molecular docking study was performed using Glide program to establish the information about the interactions between the topoisomerase DNA gyrase enzymes and the novel compound in order to explore the biological behaviour of the examined compound. The compound screened against four pathogens two gram positive, two gram negative and two fungal strains had shown good anti-bacterial and antifungal behaviour. Furthermore the compound was subjected to in-silico ADMET studies.Communicated by Ramaswamy H. Sarma.

Chemical characterization and DPP-IV inhibitory activity evaluation of tripeptides from Gynura divaricata (L.) DC

ETHNOPHARMACOLOGICAL RELEVANCE

Gynura divaricata (L.) DC. (GD), a herbal medicine, has been used for the prevention and treatment of hyperglycemia in China. However, hypoglycemic ingredients within GD have not yet been well studied.

AIM OF THE STUDY

The aim of this study was to explore undiscovered compounds with dipeptidyl peptidase IV (DPP-IV) inhibitory activity within GD.

MATERIALS AND METHODS

A four-step strategy was developed to explore undiscovered DPP-IV inhibitors within GD. First, the components were preliminarily characterized using UHPLC-HRMS combined with a library search. Second, preliminarily characterized compounds were searched for potential bioactivity. Third, a mixture of these preliminarily characterized compounds was isolated and thoroughly characterized based on fragmentation patterns associated with molecular networking. Fourth, the activities of these compounds were verified using DPP-IV inhibitory assay and molecular docking.

RESULTS

Diprotin A, a tripeptide inhibitor against DPP-IV, was identified. Thereafter, a mixture of twenty-five diprotin A analogs was isolated and characterized, which exhibited IC₅₀ of 0.40 mg/mL for DPP-IV. Molecular docking results also confirmed the interactions between the tripeptide analogs and DPP-IV mainly via H-bonds and hydrophobic interactions.

CONCLUSIONS

This is the first report of DPP-IV inhibitors within GD. These findings demonstrate that the extract of GD might be beneficial for the treatment of type 2 diabetes mellitus, and is expected to promote further development and utilization of GD in herbal medicine.

An advanced network pharmacology study to explore the novel molecular mechanism of Compound Kushen Injection for treating hepatocellular carcinoma by bioinformatics and experimental verification

Background

Compound Kushen Injection (CKI) is a Chinese patent drug that exerts curative effects in the clinical treatment of hepatocellular carcinoma (HCC). This study aimed to explore the targets and potential pharmacological mechanisms of CKI in the treatment of HCC.

Methods

In this study, network pharmacology was used in combination with molecular biology experiments to predict and verify the molecular mechanism of CKI in the treatment of HCC. The constituents of CKI were identified by UHPLC-MS/MS and literature search. The targets corresponding to these compounds and the targets related to HCC were collected based on public databases. To screen out the potential hub targets of CKI in the treatment of HCC, a compound-HCC target network was constructed. The underlying pharmacological mechanism was explored through the subsequent enrichment analysis. Interactive Gene Expression Profiling Analysis and Kaplan-Meier plotter were used to examine the expression and prognostic value of hub genes. Furthermore, the effects of CKI on HCC were verified through molecular docking simulations and cell experiments in vitro.

Results

Network analysis revealed that BCHE, SRD5A2, EPHX2, ADH1C, ADH1A and CDK1 were the key targets of CKI in the treatment of HCC. Among them, only CDK1 was highly expressed in HCC tissues, while the other 5 targets were lowly expressed. Furthermore, the six hub genes were all closely related to the prognosis of HCC patients in survival analysis. Molecular docking revealed that there was an efficient binding potential between the constituents of CKI and BCHE. Experiments in vitro proved that CKI inhibited the proliferation of HepG2 cells and up-regulated SRD5A2 and ADH1A, while down-regulated CDK1 and EPHX2.

Conclusions

This study revealed and verified the targets of CKI on HCC based on network pharmacology and experiments and provided a scientific reference for further mechanism research.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12906-022-03530-3.

Molecular Docking, Tyrosinase, Collagenase, and Elastase Inhibition Activities of Argan By-Products

The argan tree (Argania spinosa (L.) Skeels) is one of the most important floristic resources in Morocco. This Moroccan endemic tree is known for its numerous therapeutic and medicinal uses. In addition to some medicinal and cosmetic uses, argan fruit pulp and press cake are traditionally used by the Berber population for heating and feeding livestock. Molecular docking is an in silico approach that predicts the interaction between a ligand and a protein. This approach is mainly used in chemistry and pharmacology of natural products as a prediction tool with the purpose of selecting plant extracts or fractions for in vitro tests. The aim of this research is to study the evaluation of potential tyrosinase, collagenase, and elastase inhibitory activities of argan fruit press-cake and pulp extracts. Extracts were evaluated for their total phenolic content (TPC), and the major polyphenols of both press-cake and pulp extracts were submitted to molecular docking in order to determine the mechanisms of action of these compounds. Obtained results revealed that fruit pulp had the strongest dermocosmetic activities, as well as the highest TPC, with values above 55 mg gallic-acid equivalent per gram of dry matter (mgeq AG/gDM). Moreover, those results were positively correlated with the docking findings, suggesting that the pulp lead compounds have higher affinity with tyrosinase, collagenase, and elastase action sites. The results here presented are very promising and open new perspectives for the exploitation of argan-tree by-products as cosmetic agents towards the development of new anti-aging products.

Use of In silico tools for screening buffers to overcome physical instability of Abatacept

Rheumatoid arthritis is an autoimmune disorder. Abatacept (CTLA4-Ig) is used for the treatment of Rheumatoid arthritis. Abatacept is a monoclonal antibody. Monoclonal antibodies undergo chemical (e.g. oxidation, deamidation, hydrolysis) and physical (e.g. aggregation, unfolding) instabilities while handling and storage. Abatacept is also prone to aggregation. Stabilizing agents such as buffers are used to stabilize monoclonal antibodies. But, the selection of the appropriate buffer is a time-consuming process because after testing many buffers based on the analysis of the results the appropriate buffer is identified. To overcome this issue in the current study computational tools were utilized to virtually screen different buffers to select the appropriate buffer. Ligand binding is the principal mechanism of conformational stability of proteins. For the buffers as well ligand binding is the most common mechanism for enhancing the thermodynamic stability of proteins. Generally it is observed that by enhancing the thermodynamic stability there is reduction in the rate of aggregation of proteins. Buffer (ligand) binds to the native state of the protein preferentially; it results in stabilization of the protein, while in the case of denatured protein it has no impact. There are many studies conducted involving the proteins in buffer solutions but very limited information is available about the mechanism of protein-buffer interactions. In the current study ligand binding mechanism of protein - buffer interaction was studied using molecular docking. After the docking buffers were ranked according to their energy value. The lower energy scores represent better protein-buffer (ligand) binding affinity compared to high energy values. It was observed that Phosphate with a binding affinity of -107.9 kcal/mol was the buffer with the least binding energy followed by Citrate (-70.6 kcal/mol), Melglumine (-66.6 kcal/mol), Arginine (-64.5 kcal/mol), Glucono delta lactone (-62.6 kcal/mol), Sodium citrate (-56.5 kcal/mol), Tromethamine (-52.3 kcal/mol), Glycine HCl (-37.2 kcal/mol), Sulfuric acid (-37.7 kcal/mol), Ammonium acetate (-31.1 kcal/mol), Acetic acid (-30.7 kcal/mol). With lower binding energy higher is the affinity between the ligand and protein. So phosphate was identified as a buffer with the highest affinity with Abatacept.

Simulation studies, 3D QSAR and molecular docking on a point mutation of protein kinase B with flavonoids targeting ovarian Cancer

BACKGROUND

Ovarian cancer is the world's dreaded disease and its prevalence is expanding globally. The study of integrated molecular networks is crucial for the basic mechanism of cancer cells and their progression. During the present investigation, we have examined different flavonoids that target protein kinases B (AKT1) protein which exerts their anticancer efficiency intriguing the role in cross-talk cell signalling, by metabolic processes through in-silico approaches.

METHOD

Molecular dynamics simulation (MDS) was performed to analyze and evaluate the stability of the complexes under physiological conditions and the results were congruent with molecular docking. This investigation revealed the effect of a point mutation (W80R), considered based on their frequency of occurrence, with AKT1 protein.

RESULTS

The ligand with high docking scores and favourable behaviour on dynamic simulations are proposed as potential W80R inhibitors. A virtual screening analysis was performed with 12,000 flavonoids satisfying Lipinski's rule of five according to which drug-likeness is predicted based on its pharmacological and biological properties to be active and taken orally. The pharmacokinetic ADME (adsorption, digestion, metabolism, and excretion) studies featured drug-likeness. Subsequently, a statistically significant 3D-QSAR model of high correlation coefficient (R2) with 0.822 and cross-validation coefficient (Q2) with 0.6132 at 4 component PLS (partial least square) were used to verify the accuracy of the models. Taxifolin holds good interactions with the binding domain of W80R, highest Glide score of - 9.63 kcal/mol with OH of GLU234 and H bond ASP274 and LEU156 amino acid residues and one pi-cation interaction and one hydrophobic bond with LYS276.

CONCLUSION

Natural compounds have always been a richest source of active compounds with a wide variety of structures, therefore, these compounds showed a special inspiration for medical chemists. The present study has aimed molecular docking and molecular dynamics simulation studies on taxifolin targeting W80R mutant protein of protein kinase B/serine- threonine kinase/AKT1 (EC:2.7.11.1) protein of ovarian cancer for designing therapeutic intervention. The expected result supported the molecular cause in a mutant form which resulted in a gain of ovarian cancer. Here we discussed validations computationally and yet experimental evaluation or in vivo studies are endorsed for further study. Several of these compounds should become the next marvels for early detection of ovarian cancer.

Microscopic interactions between ivermectin and key human and viral proteins involved in SARS-CoV-2 infection

The identification of chemical compounds able to bind specific sites of the human/viral proteins involved in the SARS-CoV-2 infection cycle is a prerequisite to design effective antiviral drugs. Here we conduct a molecular dynamics study with the aim to assess the interactions of ivermectin, an antiparasitic drug with broad-spectrum antiviral activity, with the human Angiotensin-Converting Enzyme 2 (ACE2), the viral 3CL^pro and PL^pro proteases, and the viral SARS Unique Domain (SUD). The drug/target interactions have been characterized in silico by describing the nature of the non-covalent interactions found and by measuring the extent of their time duration along the MD simulation. Results reveal that the ACE2 protein and the ACE2/RBD aggregates form the most persistent interactions with ivermectin, while the binding with the remaining viral proteins is more limited and unspecific.

Theoretical and experimental study of interaction of macroheterocyclic compounds with ORF3a of SARS-CoV-2

The pandemic infectious disease (Covid-19) caused by the coronavirus (SARS-CoV2) is spreading rapidly around the world. Covid-19 does an irreparable harm to the health and life of people. It also has a negative financial impact on the economies of most countries of the world. In this regard, the issue of creating drugs aimed at combating this disease is especially acute. In this work, molecular docking was used to study the docking of 23 compounds with QRF3a SARS-CoV2. The performed in silico modeling made it possible to identify leading compounds capable of exerting a potential inhibitory and virucidal effect. The leading compounds include chlorin (a drug used in PDT), iron(III)protoporphyrin (endogenous porphyrin), and tetraanthraquinone porphyrazine (an exogenous substance). Having taken into consideration the localization of ligands in the QRF3a SARS-CoV2, we have made an assumption about their influence on the pathogenesis of Covid-19. The interaction of chlorin, iron(III)protoporphyrin and protoporphyrin with the viral protein ORF3a were studied by fluorescence and UV–Vis spectroscopy. The obtained experimental results confirm the data of molecular docking. The results showed that a viral protein binds to endogenous porphyrins and chlorins, moreover, chlorin is a competitive ligand for endogenous porphyrins. Chlorin should be considered as a promising drug for repurposing.

Molecular Mechanism Exploration of Potent Fluorinated PI3K Inhibitors with a Triazine Scaffold: Unveiling the Unusual Synergistic Effect of Pyridine-to-Pyrimidine Ring Interconversion and CF3 Defluorination

The phosphatidylinostitol-3-kinase (PI3K)/AKT/mammalian target of rapamycin signaling pathway is a vital regulator of cell proliferation, growth, and survival, which is frequently overactivated in many human cancers. To this effect, PI3K, which is an important mediator of this pathway, has been pinpointed as a crucial target in cancer therapy and hence the importance of PI3K inhibitors. It was recently reported that defluorination and pyridine-to-pyrimidine ring interconversion increase the potency of specific small-molecule inhibitors of PI3K. Compound 4, an inhibitor with the difluorinated pyrimidine motif, was found to be eight times more potent against PI3K than compound 1, an inhibitor with the trifluorinated pyridine motif. This observation presents the need to rationally resolve the differential inhibitory mechanisms exhibited by both compounds. In this present work, we employed multiple computational approaches to investigate and distinguish the binding modes of 1 and 4 in addition to the effects they mediate on the secondary structure of PI3K. Likewise, we evaluated two other derivatives, compounds 2 with the difluorinated pyridine motif and 3 with the trifluorinated pyrimidine motif, to investigate the cooperativity effect between the defluorination of CF₃ and pyridine-to-pyrimidine ring interconversion. Findings revealed that PI3K, upon interaction with 4, exhibited a series of structural changes that favored the binding of the inhibitor at the active-site region. Furthermore, a positive (synergistic) cooperativity effect was observed between CF₃ defluorination and pyridine-to-pyrimidine ring interconversion. Moreover, there was a good correlation between the binding free energy estimated and the biological activity reported experimentally. Energy decomposition analysis revealed that the major contributing force to binding affinity variations between 1 and 4 is the electrostatic energy. Per-residue energy-based hierarchical clustering analysis further identified four hot-spot residues ASP841, TYR867, ASP964, and LYS833 and four warm-spot residues ASP836, SER806, ASP837, and LYS808, which essentially mediate the optimal and higher-affinity binding of compound 4 to PI3K relative to 1. This study therefore provides rational insights into the mechanisms by which 4 exhibited superior PI3K-inhibitory activities over 1, which is vital for future structure-based drug discovery efforts in PI3K targeting.

A Computational Perspective on Molecular Recognition by Galectins

This article presents an overview of recent computational studies dedicated to the analysis of binding between galectins and small-molecule ligands. We first present a summary of the most popular simulation techniques adopted for calculating binding poses and binding energies, and then discuss relevant examples reported in the literature for the three main classes of galectins (dimeric, tandem and chimera). We show that simulation of galectin-ligand interactions is a mature field which has proven invaluable for completing and unraveling experimental observations. Future perspectives to further improve the accuracy and cost-effectiveness of existing computational approaches will involve the development of new schemes to account for solvation and entropy effects, which represent the main current limitations to the accuracy of computational results.

Computational drug repurposing study of antiviral drugs against main protease, RNA polymerase, and spike proteins of SARS-CoV-2 using molecular docking method

OBJECTIVES

The new Coronavirus (SARS-CoV-2) created a pandemic in the world in late 2019 and early 2020. Unfortunately, despite the increasing prevalence of the disease, there is no effective drug for the treatment. A computational drug repurposing study would be an appropriate and rapid way to provide an effective drug in the treatment of the coronavirus disease of 2019 (COVID-19) pandemic. In this study, the inhibitory potential of more than 50 antiviral drugs on three important proteins of SARS-CoV-2, was investigated using the molecular docking method.

METHODS

By literature review, three important proteins, including main protease, RNA-dependent RNA polymerase (RdRp), and spike, were selected as the drug targets. The three-dimensional (3D) structure of protease, spike, and RdRp proteins was obtained from the Protein Data Bank. Proteins were energy minimized. More than 50 antiviral drugs were considered as candidates for protein inhibition, and their 3D structure was obtained from Drug Bank. Molecular docking settings were defined using Autodock 4.2 software and the algorithm was executed.

RESULTS

Based on the estimated binding energy of docking and hydrogen bond analysis and the position of drug binding, five drugs including, indinavir, lopinavir, saquinavir, nelfinavir, and remdesivir, had the highest inhibitory potential for all three proteins.

CONCLUSIONS

According to the results, among the mentioned drugs, saquinavir and lopinavir showed the highest inhibitory potential for all three proteins compared to the other drugs. This study suggests that saquinavir and lopinavir could be included in the laboratory phase studies as a two-drug treatment for SARS-CoV-2 inhibition.

Multitask deep networks with grid featurization achieve improved scoring performance for protein-ligand binding

Deep learning-based methods have been extensively developed to improve scoring performance in structure-based drug discovery. Extending multitask deep networks in addressing pharmaceutical problems shows remarkable improvements over single task network. Recently, grid featurization has been introduced to convert protein-ligand complex co-ordinates into fingerprints with the advantage of incorporating inter- and intra-molecular information. The combination of grid featurization with multitask deep networks would hold great potential to boost the scoring performance. We examined the performance of three novel multitask deep networks (standard multitask, bypass, and progressive network) in reproducing the binding affinities of protein-ligand complexes in comparison with AutoDock Vina docking and MM/GBSA method. Among five evaluated methods, progressive network combined with grid featurization provided the best Pearson correlation coefficient (0.74) and least mean absolute average error (0.98) for the overall scoring performance. Moreover, all networks increased screening ability for the re-docking pose and progressive network even achieved AUC of 0.87 over 0.52 of AutoDock Vina. Our results demonstrated that progressive network combined with grid featurization would be one powerful rescoring approach to strengthen screening results after obtaining protein-ligand complex in the conventional docking software.