Bridging Molecular Docking to Molecular Dynamics in Exploring Ligand-Protein Recognition Process: An Overview

Experimental, molecular docking and molecular dynamic studies of natural products targeting overexpressed receptors in breast cancer

Natural compounds are proper tools for inhibiting cancer cell proliferation. Hence, the search for these ligands of overexpressed receptors in breast cancer has been a competitive challenge recently and opens new avenues for drug discovery. In this research, we have investigated molecular interactions between natural products and overexpressed receptors in breast cancer using molecular docking and dynamic simulation approaches followed by extraction of the best ligand from Citrus limetta and developing for nanoscale encapsulation composed of soy lecithin using a sonicator machine. The encapsulation process was confirmed by DLS and TEM analyses. Anticancer activity was also examined using MTT method. Among the investigated natural compounds, hesperidin was found to bind to specific targets with stronger binding energy. The molecular dynamics results indicated that the hesperidin-MCL-1 complex is very stable at 310.15 K for 200 ns. The RP-HPLC analysis revealed that the purity of extracted hesperidin was 98.8% with a yield of 1.72%. The results of DLS and TEM showed a strong interaction between hesperidin and lecithin with an entrapped efficiency of 92.02 ± 1.08%. Finally, the cytotoxicity effect of hesperidin was increased against the MDA-MB-231 cell line with an IC50 value of 62.93 μg/mL after encapsulation, whereas no significant effect against the MCF10A cell line. We showed for the first time that hesperidin is a flexible and strong ligand for the MCL-1 receptor. Also, it has the in vitro ability to kill the MDA-MB-231 cell lines without having a significant effect on the MCF10A cell lines. Therefore, hesperidin could be used as a food ingredient to generate functional foods.

TeachOpenCADD 2022: open source and FAIR Python pipelines to assist in structural bioinformatics and cheminformatics research

Computational pipelines have become a crucial part of modern drug discovery campaigns. Setting up and maintaining such pipelines, however, can be challenging and time-consuming—especially for novice scientists in this domain. TeachOpenCADD is a platform that aims to teach domain-specific skills and to provide pipeline templates as starting points for research projects. We offer Python-based solutions for common tasks in cheminformatics and structural bioinformatics in the form of Jupyter notebooks, based on open source resources only. Including the 12 newly released additions, TeachOpenCADD now contains 22 notebooks that cover both theoretical background as well as hands-on programming. To promote reproducible and reusable research, we apply software best practices to our notebooks such as testing with automated continuous integration and adhering to the idiomatic Python style. The new TeachOpenCADD website is available at https://projects.volkamerlab.org/teachopencadd and all code is deposited on GitHub.

Machine learning assessment of the binding region as a tool for more efficient computational receptor-ligand docking

We present a combined computational approach to protein-ligand binding, which consists of two steps: (1) a deep neural network is used to locate a binding region on a target protein, and (2) molecular docking of a ligand is performed within the specified region to obtain the best pose using Autodock Vina. Our in-house designed neural network was trained using the PepBDB dataset. Although the training dataset consisted of protein-peptide complexes, we show that the approach is not limited to peptides, but also works remarkably well for a large class of non-peptide ligands. The results are compared with those in which the binding region (first step) was provided by Accluster. In cases where no prior experimental data on the binding region are available, our deep neural network provides a fast and effective alternative to classical software for its localization. Our code is available at https://github.com/mksmd/NNforDocking.

A lesson for the maestro of the replication fork: Targeting the protein-binding interface of proliferating cell nuclear antigen for anticancer therapy

The proliferating cell nuclear antigen (PCNA) has emerged as a promising candidate for the development of novel cancer therapeutics. PCNA is a nononcogenic mediator of DNA replication that regulates a diverse range of cellular functions and pathways through a comprehensive list of protein-protein interactions. The hydrophobic binding pocket on PCNA offers an opportunity for the development of inhibitors to target various types of cancers and modulate protein-protein interactions. In the present study, we explored the binding modes and affinity of molecule I1 (standard molecule) with the previously suggested dimer interface pocket and the hydrophobic pocket present on the frontal side of the PCNA monomer. We also identified potential lead molecules from the library of in-house synthesized 3-methylenisoindolin-1-one based molecules to inhibit the protein-protein interactions of PCNA. Our results were based on robust computational methods, including molecular docking, conventional, steered, and umbrella sampling molecular dynamics simulations. Our results suggested that the standard inhibitor I1 interacts with the hydrophobic pocket of PCNA with a higher affinity than the previously suggested binding site. Also, the proposed molecules showed better or comparable binding free energies as calculated by the Molecular Mechanics Poisson-Boltzmann Surface Area (MMPBSA) approach and further validated by enhanced umbrella sampling simulations. In vitro and in vivo methods could test the computationally suggested molecules for advancement in the drug discovery pipeline.

Design, Synthesis and Evaluation of Fused Bicyclo[2.2.2]octene as a Potential Core Scaffold for the Non-Covalent Inhibitors of SARS-CoV-2 3CLpro Main Protease

The emergence of SARS-CoV-2, responsible for the global COVID-19 pandemic, requires the rapid development of novel antiviral drugs that would contribute to an effective treatment alongside vaccines. Drug repurposing and development of new molecules targeting numerous viral targets have already led to promising drug candidates. To this end, versatile molecular scaffolds with high functionalization capabilities play a key role. Starting with the clinically used conformationally flexible HIV-1 protease inhibitors that inhibit replication of SARS-CoV-2 and bind major protease 3CL^pro, we designed and synthesized a series of rigid bicyclo[2.2.2]octenes fused to N-substituted succinimides to test whether this core scaffold could support the development of non-covalent 3CL^pro inhibitors. Inhibition assays confirmed that some compounds can inhibit the SARS-CoV-2 main protease; the most promising compound 11a inhibited 3CL^pro in micromolar range (IC₅₀ = 102.2 μM). Molecular simulations of the target-ligand complex in conjunction with dynophore analyses and endpoint free energy calculations provide additional insight and first recommendations for future optimization. The fused bicyclo[2.2.2]octenes can be used as a new potential starting point in the development of non-covalent SARS-CoV-2 3CL^pro protease inhibitors and the study also substantiates the potential of this versatile scaffold for the development of biologically active molecules.

In Silico Modeling and Scoring of PROTAC-Mediated Ternary Complex Poses

Proteolysis targeting chimeras (PROTACs) are molecules that induce protein degradation via formation of ternary complexes between an E3 ubiquitin ligase and a target protein. The rational design of PROTACs requires accurate knowledge of the native configuration of the PROTAC-induced ternary complex. This study demonstrates that native and non-native ternary complex poses can be distinguished based on the pose occupancy time in MD, where native poses exhibit longer occupancy times at both room and higher temperatures. Candidate poses are generated by MD sampling and pre-ranked by classic MM/GBSA. A specific heating scheme is then applied to accelerate ternary pose departure, with the pose occupancy time and fraction being measured. This scoring identifies the native pose in all systems tested. Its success is partially attributed to the dynamic nature of pose departure analyses, which accounts for entropic effects typically neglected in the faster static scoring methods, while entropy plays a greater role in protein-protein than in protein-ligand systems.

Repurposing of phytomedicine-derived bioactive compounds with promising anti-SARS-CoV-2 potential: Molecular docking, MD simulation and drug-likeness/ADMET studies

In view of the potential of traditional plant-based remedies (or phytomedicines) in the management of COVID-19, the present investigation was aimed at finding novel anti-SARS-CoV-2 molecules by in silico screening of bioactive phytochemicals (database) using computational methods and drug repurposing approach. A total of 160 compounds belonging to various phytochemical classes (flavonoids, limonoids, saponins, triterpenoids, steroids etc.) were selected (as initial hits) and screened against three specific therapeutic targets (Mpro/3CLpro, PLpro and RdRp) of SARS-CoV-2 by docking, molecular dynamics simulation and drug-likeness/ADMET studies. From our studies, six phytochemicals were identified as notable ant-SARS-CoV-2 agents (best hit molecules) with promising inhibitory effects effective against protease (Mpro and PLpro) and polymerase (RdRp) enzymes. These compounds are namely, ginsenoside Rg2, saikosaponin A, somniferine, betulinic acid, soyasapogenol C and azadirachtin A. On the basis of binding modes and dynamics studies of protein-ligand intercations, ginsenoside Rg2, saikosaponin A, somniferine were found to be the most potent (in silico) inhibitors potentially active against Mpro, PLpro and RdRp, respectively. The present investigation can be directed towards further experimental studies in order to confirm the anti-SARS-CoV-2 efficacy along with toxicities of identified phytomolecules.

The Resistance of Cancer Cells to Palbociclib, a Cyclin-Dependent Kinase 4/6 Inhibitor, is Mediated by the ABCB1 Transporter

Palbociclib was approved by the United States Food and Drug Administration for use, in combination with letrozole, as a first-line treatment for estrogen receptor-positive/human epidermal growth factor receptor 2-negative (ER+/HER2-) postmenopausal metastatic breast cancer. However, recent studies show that palbociclib may be an inhibitor of the ABCB1 transporter, although this remains to be elucidated. Therefore, we conducted experiments to determine the interaction of palbociclib with the ABCB1 transporter. Our in vitro results indicated that the efficacy of palbociclib was significantly decreased in the ABCB1-overexpressing cell lines. Furthermore, the resistance of ABCB1-overexpressing cells to palbociclib was reversed by 3 μM of the ABCB1 inhibitor, verapamil. Moreover, the incubation of ABCB1-overexpressing KB-C2 and SW620/Ad300 cells with up to 5 μM of palbociclib for 72 h, significantly upregulated the protein expression of ABCB1. The incubation with 3 µM of palbociclib for 2h significantly increased the intracellular accumulation of [³H]-paclitaxel, a substrate of ABCB1, in ABCB1 overexpressing KB-C2 cells but not in the corresponding non-resistant parental KB-3-1 cell line. However, the incubation of KB-C2 cells with 3 μM of palbociclib for 72 h decreased the intracellular accumulation of [³H]-paclitaxel due to an increase in the expression of the ABCB1 protein. Palbociclib produced a concentration-dependent increase in the basal ATPase activity of the ABCB1 transporter (EC₅₀ = 4.73 μM). Molecular docking data indicated that palbociclib had a high binding affinity for the ABCB1 transporter at the substrate binding site, suggesting that palbociclib may compete with other ABCB1 substrates for the substrate binding site of the ABCB1. Overall, our results indicate that palbociclib is a substrate for the ABCB1 transporter and that its in vitro anticancer efficacy is significantly decreased in cancer cells overexpressing the ABCB1.

Computational investigation of aphid odorant receptor structure and binding function

Odorant receptors (OR) play a critical role in signal transduction and olfactory recognition in insects. Unfortunately, insect ORs are difficult to express and purify, and limited structural data are available. Computational methods were used to predict models for aphid ORs, and binding interactions with aphid pheromones and other semiochemicals were investigated. Previously functionally characterised ORs from the pea aphid, Acyrthosiphon pisum, ApisOR4 and ApisOR5, were screened against functional ligands. ApisOR5 had a defined binding site, and had predicted interactions with the aphid alarm pheromone, (E)-β-farnesene. ApisOR4 had multiple distinct binding sites and showed broad tuning to multiple odorants. Screening of six other highly conserved ORs showed some interactions and potential enantiomeric discrimination between the aphid sex pheromone components (4aS,7S,7aR)-nepetalactone and (1R,4aS,7S,7aR)-nepetalactol. These results indicate that specific binding sites may be more critical to understanding olfactory activity of ligands and ORs than kinetic data, and greater knowledge of the method of action of ORs is required.Communicated by Ramaswamy H. Sarma.

WGCNA and molecular docking reveal key hub genes and potential natural inhibitor in interstitial cystitis/bladder pain syndrome

INTRODUCTION AND HYPOTHESIS

The etiology and treatment of interstitial cystitis/bladder pain syndrome are still controversial. The purpose of this study is to determine the key genes and specific regulatory pathways related to it and to find potential drug-active components through integrated bioinformatics.

METHODS

The data set GSE11783 was downloaded from GEO database. The modules significantly related to interstitial cystitis/bladder pain syndrome were identified by weighted correlation network analysis. The genes in the key modules were analyzed by functional enrichment and protein interaction by Cytoscape software, and finally the core hub genes were screened. Furthermore, the molecular docking verification of active components and key proteins was carried out by using AutoDock Vin software.

RESULTS

Among the 14 modules derived from WGCNA, turquoise module had the highest correlation with IC/BPS (r = 0.85, P < 0.001). The genes in the module were mainly enriched in the biological processes such as the interaction between cytokines and cytokine receptors and chemokine signaling pathway. The genes in the related modules of differentially expressed genes and WGCNA traits were intersected to obtain the core hub genes. Protein-protein interaction network analysis showed that the key genes were upregulated genes CCR7 and CCL19. In terms of molecular docking, triptolide, the active component in the traditional anti-inflammatory drug Tripterygium wilfordii, can form effective molecular binding with both core hub genes.

CONCLUSIONS

Our study identified the core hub genes CCR7 and CCL19, which acted as essential components in interstitial cystitis/bladder pain syndrome. Furthermore, CCR7 and CCL19 can form effective binding with triptolide, which will provide new insights into the development of new therapies for interstitial cystitis/bladder pain syndrome.

Elucidating the binding mechanism of an antimigraine agent with a model protein: insights from molecular spectroscopic, calorimetric and computational approaches

Sumatriptan (SUM), a serotonin activator used to treat migraines and cluster headaches. Molecular spectroscopic methods including fluorescence quenching, time dependent fluorescence, FRET, absorption, circular dichroism, differential scanning calorimetric and computational approaches were employed to unravel the interaction between sumatriptan and bovine serum albumin (BSA). The fluorescence quenching studies suggested the interaction between SUM and BSA with a moderate binding with the binding constant (K_b) in the order of 10⁴. The findings of temperature and time dependent fluorescence quenching studies confirmed the role of static quenching mechanism. Thermodynamic parameters suggested the key role of electrostatic force in the interaction of SUM with BSA. Absorption and CD spectral studies revealed the bioenvironmental changes around the Trp in BSA upon binding of SUM. Calorimetric based thermal denaturation results confirmed that the thermal stability of BSA was improved in the presence of SUM. resulted in the this decreased flexibility of protein chain. Site competitive studies indicated SUM was located in the hydrophobic cavity of site I which was further confirmed by the docking and dynamic simulation studies. Additionally, molecular dynamics simulations inferred the microenvironmental condition around the SUM and the amino acids and forces involved in the binding of SUM with BSA.Communicated by Ramaswamy H. Sarma.

Combined Modeling Study of the Binding Characteristics of Natural Compounds, Derived from Psoralea Fruits, to β-Amyloid Peptide Monomer

A dysfunctional protein aggregation in the nervous system can lead to several neurodegenerative disorders that result in intracellular inclusions or extracellular aggregates. An early critical event within the pathogenesis of Alzheimer’s disease is the accumulation of amyloid beta peptide within the brain. Natural compounds isolated from Psoralea Fructus (PF) have significant anti-Alzheimer effects as strong inhibitors of Aβ42 aggregation. Computer simulations provide a powerful means of linking experimental findings to nanoscale molecular events. As part of this research four prenylated compounds, the active ingredients of Psoralea Fructus (PF), were studied as Aβ42 accumulation inhibitors using molecular simulations modeling. In order to resolve the binding modes of the ligands and identify the main interactions of Aβ42 residues, we performed a 100 ns molecular dynamics simulation and binding free energy calculations starting from the model of the compounds obtained from the docking study. This study was able to pinpoint the key amino acid residues in the Aβ42 active site and provide useful information that could benefit the development of new Aβ42 accumulation inhibitors.

Molecular simulations of proteins: From simplified physical interactions to complex biological phenomena

Molecular dynamics simulation is the most popular computational technique for investigating the structural and dynamical behaviour of proteins, in search of the molecular basis of their function. Far from being a completely settled field of research, simulations are still evolving to best capture the essential features of the atomic interactions that govern a protein's inner motions. Modern force fields are becoming increasingly accurate in providing a physical description adequate to this purpose, and allow us to model complex biological systems under fairly realistic conditions. Furthermore, the use of accelerated sampling techniques is improving our access to the observation of progressively larger molecular structures, longer time scales, and more hidden functional events. In this review, the basic principles of molecular dynamics simulations and a number of key applications in the area of protein science are summarized, and some of the most important results are discussed. Examples include the study of the structure, dynamics and binding properties of 'difficult' targets, such as intrinsically disordered proteins and membrane receptors, and the investigation of challenging phenomena like hydration-driven processes and protein aggregation. The findings described provide an overall picture of the current state of this research field, and indicate new perspectives on the road ahead to the upcoming future of molecular simulations.

Molecular Interactions of Tannic Acid with Proteins Associated with SARS-CoV-2 Infectivity

The overall impact of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) on our society is unprecedented. The identification of small natural ligands that could prevent the entry and/or replication of the coronavirus remains a pertinent approach to fight the coronavirus disease (COVID-19) pandemic. Previously, we showed that the phenolic compounds corilagin and 1,3,6-tri-O-galloyl-β-D-glucose (TGG) inhibit the interaction between the SARS-CoV-2 spike protein receptor binding domain (RBD) and angiotensin-converting enzyme 2 (ACE2), the SARS-CoV-2 target receptor on the cell membrane of the host organism. Building on these promising results, we now assess the effects of these phenolic ligands on two other crucial targets involved in SARS-CoV-2 cell entry and replication, respectively: transmembrane protease serine 2 (TMPRSS2) and 3-chymotrypsin like protease (3CLpro) inhibitors. Since corilagin, TGG, and tannic acid (TA) share many physicochemical and structural properties, we investigate the binding of TA to these targets. In this work, a combination of experimental methods (biochemical inhibition assays, surface plasmon resonance, and quartz crystal microbalance with dissipation monitoring) confirms the potential role of TA in the prevention of SARS-CoV-2 infectivity through the inhibition of extracellular RBD/ACE2 interactions and TMPRSS2 and 3CLpro activity. Moreover, molecular docking prediction followed by dynamic simulation and molecular mechanics Poisson-Boltzmann surface area (MMPBSA) free energy calculation also shows that TA binds to RBD, TMPRSS2, and 3CLpro with higher affinities than TGG and corilagin. Overall, these results suggest that naturally occurring TA is a promising candidate to prevent and inhibit the infectivity of SARS-CoV-2.

Molecular Docking as a Therapeutic Approach for Targeting Cancer Stem Cell Metabolic Processes

Cancer stem cells (CSCs) are subpopulation of cells which have been demonstrated in a variety of cancer models and involved in cancer initiation, progression, and development. Indeed, CSCs which seem to form a small percentage of tumor cells, display resembling characteristics to natural stem cells such as self-renewal, survival, differentiation, proliferation, and quiescence. Moreover, they have some characteristics that eventually can demonstrate the heterogeneity of cancer cells and tumor progression. On the other hand, another aspect of CSCs that has been recognized as a central concern facing cancer patients is resistance to mainstays of cancer treatment such as chemotherapy and radiation. Owing to these details and the stated stemness capabilities, these immature progenitors of cancerous cells can constantly persist after different therapies and cause tumor regrowth or metastasis. Further, in both normal development and malignancy, cellular metabolism and stemness are intricately linked and CSCs dominant metabolic phenotype changes across tumor entities, patients, and tumor subclones. Hence, CSCs can be determined as one of the factors that correlate to the failure of common therapeutic approaches in cancer treatment. In this context, researchers are searching out new alternative or complementary therapies such as targeted methods to fight against cancer. Molecular docking is one of the computational modeling methods that has a new promise in cancer cell targeting through drug designing and discovering programs. In a simple definition, molecular docking methods are used to determine the metabolic interaction between two molecules and find the best orientation of a ligand to its molecular target with minimal free energy in the formation of a stable complex. As a comprehensive approach, this computational drug design method can be thought more cost-effective and time-saving compare to other conventional methods in cancer treatment. In addition, increasing productivity and quality in pharmaceutical research can be another advantage of this molecular modeling method. Therefore, in recent years, it can be concluded that molecular docking can be considered as one of the novel strategies at the forefront of the cancer battle via targeting cancer stem cell metabolic processes.

Anti-Cancer Properties of Stevia rebaudiana; More than a Sweetener

Stevia rebaudiana Bertoni is a perennial shrub from Paraguay that is nowadays widely cultivated, since it is increasingly being utilized as a sugar substitute in various foodstuffs due to its sweetness and minimal caloric content. These properties of the plant’s derivatives have spurred research on their biological activities revealing a multitude of benefits to human health, including antidiabetic, anticariogenic, antioxidant, hypotensive, antihypertensive, antimicrobial, anti-inflammatory and antitumor actions. To our knowledge, no recent reviews have surveyed and reported published work solely on the latter. Consequently, our main objective was to present a concise, literature-based review of the biological actions of stevia derivatives in various tumor types, as studied in in vitro and in vivo models of the disease. With global cancer estimates suggesting a 47% increase in cancer cases by 2040 compared to 2020, the data reviewed in this article should provide a better insight into Stevia rebaudiana and its products as a means of cancer prevention and therapy within the context of a healthy diet.

Computational Simulations Highlight the IL2Rα Binding Potential of Polyphenol Stilbenes from Fenugreek

Widely used in global households, fenugreek is well known for its culinary and medicinal uses. The various reported medicinal properties of fenugreek are by virtue of the different natural phytochemicals present in it. Regarded as a promising target, interleukin 2 receptor subunit alpha (IL2Rα) has been shown to influence immune responses. In the present research, using in silico techniques, we have demonstrated the potential IL2Rα binding properties of three polyphenol stilbenes (desoxyrhaponticin, rhaponticin, rhapontigenin) from fenugreek. As the first step, molecular docking was performed to assess the binding potential of the fenugreek phytochemicals with IL2Rα. All three phytochemicals demonstrated interactions with active site residues. To confirm the reliability of our molecular docking results, 100 ns molecular dynamics simulations studies were undertaken. As discerned by the RMSD and RMSF analyses, IL2Rα in complex with the desoxyrhaponticin, rhaponticin, and rhapontigenin indicated stability. The RMSD analysis of the phytochemicals alone also demonstrated no significant structural changes. Based on the stable molecular interactions and comparatively slightly better MM/PBSA binding free energy, rhaponticin seems promising. Additionally, ADMET analysis performed for the stilbenes indicated that all of them obey the ADMET rules. Our computational study thus supports further in vitro IL2Rα binding studies on these stilbenes, especially rhaponticin.

In Silico Studies to Develop New GSK3β Inhibitors Effective in Alzheimer's Disease

Background:

Alzheimer's disease affects a large part of the world population by prolonging the human life span and becoming an economic burden in the health system. Therefore, its treatment becomes more and more important every day. With the insufficiency of existing drug molecules, new drug targets started to be emphasized. The most important of these is the Glycogen Synthase Kinase 3β enzyme, thought to be of key importance in Tau hyperphosphorylation and Amyloid β accumulation mechanisms.

Objective:

In this research, computational studies were conducted to develop a new GSK3β enzyme inhibitor.

Method:

Leading compounds suitable for pharmacophore models obtained by the 3D QSAR method were scanned in databases. In silico ADME/Tox analyses were performed on the obtained molecules.

Results:

Although the three molecules (ENA99104, CNR13756, TIM405938) had strong Dock Scores (42.869, 53.344, and 41.119, respectively) in molecular docking calculations, only the CNR13756 molecule was found successful according to molecular dynamics simulations.

Conclusion:

All computational studies have revealed that the CNR13756 molecule can exhibit a therapeutic scaffold property, thus obtaining a selective GSK3β inhibitor with minimal side effects.

Computational approaches for evaluation of isobavachin as potential inhibitor against t877a and w741l mutations in prostate cancer

Prostate cancer is the World's second most common cancer, with the fifth-highest male mortality rate. Point mutations such as T877A and W741L are frequently seen in advanced prostate cancer patients, conferring drug-resistance and hence driving cancer growth. Such occurrence of drug resistance in prostate cancer necessitates designing of suitable ligands to ensure better interactions with the receptors which can block the progression of the disease. The present study focus on the modification of plant-derived flavonoids that might act as inhibitors against such point mutations namely, T877A and W741L. In T877A mutation threonine is substituted by alanine at the 877 codon and W741L mutation, tryptophan is substituted by lysine at the 741 codon in prostate cancer. The study revolved on the aspect of the evaluation of Isobavachin and its derivatives as a potential agent to tackle such point mutations by using the in silico approach. A total of 98 molecular dockings were performed to find the ligand-receptor complexes with the lowest binding energy employing Autodock Software to conduct the blind and site-specific docking. Additionally, ligands were screened for Drug-likeness and toxicity using several tools yielding eight possible drug candidates. Based on the results of Molecular Docking, Drug-likeness, and ADMET testing, ten structures, including six complexes and three receptors were subjected to molecular dynamics simulation of 100 ns covering RMSD, RMSF, Rg, and MM/PBSA. Based on the simulation results, Isobavachin, IsoMod4, and IsoMod7 were concluded to be stable and exhibited potential properties for developing a novel drug to combat prostate cancer and its associated drug-resistance.

The molecular docking and molecular dynamics study of flavonol synthase and flavonoid 3'-monooxygenase enzymes involved for the enrichment of kaempferol

Kaempferol is a natural flavonol that shows many pharmacological properties including anti-inflammatory, antioxidant, anticancer, antidiabetic activities etc. It has been reported in many vegetables, fruits, herbs and medicinal plants. The enzyme flavonol synthase (FLS, EC 1.14.20.6) catalyses the conversion of dihydroflavonols to flavonols. Whereas flavonoid 3'-monooxygenase (F3'H, EC 1.14.14.82) catalyses the hydroxylation of dihydroflavonol, and flavonol. FLS is involved in the synthesis of the kaempferol whereas F3'H causes degradation of kaempferol. The present study aimed to analyse the binding affinity, stability and activating activity of enzyme FLS as well as inhibitory activity of enzyme F3'H involved in the enrichment of the kaempferol using the in-silico approaches. Computational study for physico-chemical properties, conserved domain identification, 3-D structure prediction and its validation, conservation analysis, molecular docking followed by molecular dynamics analysis of FLS and F3'H, protein-activator (FLS-LIG Complex) and protein-inhibitor (F3'H-LIG Complex) complexes have been performed. Other structural analyses like root mean square fluctuation (RMSF), root mean square deviation (RMSD), surface area solvent accessibility (SASA), radius of gyration (Rg), hydrogen bond analysis, principal component analysis (PCA), Poisson-Boltzmann analysis (MM_PBSA) and the dynamic cross correlation map (DCCM) analysis to explore the structural, functional and thermodynamic stability of the proteins and the complexes were also studied. The molecular docking result showed that FLS binds strongly with the activator ascorbate (CID _54670067) while F3'H binds with the inhibitor ketoconazole (CID_456201). The most powerful inhibitor (ketoconazole for F3'H) and activator (ascorbate for FLS) is determined by computing the thermodynamic binding free energy through MM_PBSA analysis. The current work provides wide-ranging structural and functional information about FLS and F3'H enzymes showing detailed molecular mechanism of kaempferol biosynthesis and its degradation and hence kaempferol enrichment. Finding of the present work opens up new possibilities for future research towards enrichment of kaempferol by using activator (ascorbate) for FLS and inhibitor (ketoconazole) for F3'H as well as for its large-scale production using in vitro approaches.Communicated by Ramaswamy H. Sarma.

A multiscale approach to predict the binding mode of metallo beta-lactamase inhibitors

Antibiotic resistance is a major threat to global public health. β-lactamases, which catalyze breakdown of β-lactam antibiotics, are a principal cause. Metallo β-lactamases (MBLs) represent a particular challenge because they hydrolyze almost all β-lactams and to date no MBL inhibitor has been approved for clinical use. Molecular simulations can aid drug discovery, for example, predicting inhibitor complexes, but empirical molecular mechanics (MM) methods often perform poorly for metalloproteins. Here we present a multiscale approach to model thiol inhibitor binding to IMP-1, a clinically important MBL containing two catalytic zinc ions, and predict the binding mode of a 2-mercaptomethyl thiazolidine (MMTZ) inhibitor. Inhibitors were first docked into the IMP-1 active site, testing different docking programs and scoring functions on multiple crystal structures. Complexes were then subjected to molecular dynamics (MD) simulations and subsequently refined through QM/MM optimization with a density functional theory (DFT) method, B3LYP/6-31G(d), increasing the accuracy of the method with successive steps. This workflow was tested on two IMP-1:MMTZ complexes, for which it reproduced crystallographically observed binding, and applied to predict the binding mode of a third MMTZ inhibitor for which a complex structure was crystallographically intractable. We also tested a 12-6-4 nonbonded interaction model in MD simulations and optimization with a SCC-DFTB QM/MM approach. The results show the limitations of empirical models for treating these systems and indicate the need for higher level calculations, for example, DFT/MM, for reliable structural predictions. This study demonstrates a reliable computational pipeline that can be applied to inhibitor design for MBLs and other zinc-metalloenzyme systems.

Computational Tools for Accurate Binding Free-Energy Prediction

A quantitative thermodynamic understanding of the noncovalent association of (bio)molecules is of central importance in molecular life sciences. An important quantity characterizing (bio)molecular association is the binding affinity or absolute binding free energy. In recent years, the computational prediction of absolute binding free energies has evolved considerably in terms of accuracy, computational speed, and user-friendliness. In this chapter, we first give an overview of how absolute free energies are defined and how they can be determined with computational means. We proceed with an outline of the theoretical basis of the two most reliable methods, potential of mean force, and double decoupling calculations. In particular, we describe how the sampling problem can be alleviated by application of restraints. Finally, we provide step-by-step instructions of how to set up corresponding molecular simulations with a commonly employed molecular dynamics simulation engine.

Phytochemical Compound Screening to Identify Novel Small Molecules against Dengue Virus: A Docking and Dynamics Study

The spread of the Dengue virus over the world, as well as multiple outbreaks of different serotypes, has resulted in a large number of deaths and a medical emergency, as no viable medications to treat Dengue virus patients have yet been found. In this paper, we provide an in silico virtual screening and molecular dynamics-based analysis to uncover efficient Dengue infection inhibitors. Based on a Google search and literature mining, a large phytochemical library was generated and employed as ligand molecules. In this investigation, the protein target NS2B/NS3 from Dengue was employed, and around 27 compounds were evaluated in a docking study. Phellodendroside (−63 kcal/mole), quercimeritrin (−59.5 kcal/mole), and quercetin-7-O-rutinoside (−54.1 kcal/mole) were chosen based on their binding free energy in MM-GBSA. The tested compounds generated numerous interactions at Lys74, Asn152, and Gln167 residues in the active regions of NS2B/NS3, which is needed for the protein’s inhibition. As a result, the stable mode of docked complexes is defined by various descriptors from molecular dynamics simulations, such as RMSD, SASA, Rg, RMSF, and hydrogen bond. The pharmacological properties of the compounds were also investigated, and no toxicity was found in computational ADMET properties calculations. As a result, this computational analysis may aid fellow researchers in developing innovative Dengue virus inhibitors.

QSAR Modeling, Molecular Docking and Cytotoxic Evaluation for Novel Oxidovanadium(IV) Complexes as Colon Anticancer Agents

Four new drug-based oxidovanadium (IV) complexes were synthesized and characterized by various spectral techniques, including molar conductance, magnetic measurements, and thermogravimetric analysis. Moreover, optimal structures geometry for all syntheses was obtained by the Gaussian09 program via the DFT/B3LYP method and showed that all of the metal complexes adopted a square-pyramidal structure. The essential parameters, electrophilicity (ω) value and expression for the maximum charge that an electrophile molecule may accept (ΔN_max) showed the practical biological potency of [VO(CTZ)₂] 2H₂O. The complexes were also evaluated for their propensity to bind to DNA through UV–vis absorption titration. The result revealed a high binding ability of the [VO(CTZ)₂] 2H₂O complex with K_b = 1.40 × 10⁶ M⁻¹. Furthermore, molecular docking was carried out to study the behavior of the VO (II) complexes towards colon cancer cell (3IG7) protein. A quantitative structure–activity relationship (QSAR) study was also implemented for the newly synthesized compounds. The results of validation indicate that the generated QSAR model possessed a high predictive power (R² = 0.97). Within the investigated series, the [VO(CTZ)₂] 2H₂O complex showed the greatest potential the most selective compound comparing to the stander chemotherapy drug.

Molecular Docking as a Tool to Examine Organic Cation Sorption to Organic Matter

Molecular docking simulations were performed to examine the structural effects of organic cations on their sorption to organic matter. A set of benzylamine compounds was used to assess the sorption trends arising from the systematic structural differences between ring or nitrogen substituents. Binding simulations were performed using AutoDock 4.2 with Schulten's proposed soil organic matter as a representative organic matter structure. The calculated binding energies for the sorbate compounds correlated strongly with the measured sorption energies for Pahokee peat, indicating that the simulated binding energies and their associated sorbate orientations were representative of the experimental conditions. Graphical docking orientations showed primary, secondary, and tertiary aminium compounds to form hydrogen-bond interactions with deprotonated carboxylic acid groups in a pocket of the organic matter structure. Quaternary ammonium compounds formed pi-pi or cation-pi interactions with the aromatic groups elsewhere in the same organic matter pocket. Ring substituents showed no clear trends in sorption energies with the substituent group type for primary aminium compounds. Rather, substituent groups altered the simulated van der Waals, electrostatic, hydrogen-bond, and desolvation energy contributions to the overall sorption energies, in part because of the variations in docking orientations between compounds. Increasing methyl substitution of the aminium nitrogen group was associated with an increase in van der Waals energy contributions and a decrease in electrostatic energy contributions to the overall compound sorption energies because of aminium charge delocalization into methyl substituents and steric hindrance from methyl substituents to form specific interactions. The findings illustrate how molecular docking can be used to explore the effects of organic cation structure on sorption interactions with organic matter.

In Silico Studies of Tumor Targeted Peptide-Conjugated Natural Products for Targeting Over-Expressed Receptors in Breast Cancer Cells Using Molecular Docking, Molecular Dynamics and MMGBSA Calculations

In this work, in silico studies were carried out for the design of diterpene and polyphenol-peptide conjugates to potentially target over-expressed breast tumor cell receptors. Four point mutations were induced into the known tumor-targeting peptide sequence YHWYGYTPQN at positions 1, 2, 8 and 10, resulting in four mutated peptides. Each peptide was separately conjugated with either chlorogenate, carnosate, gallate, or rosmarinate given their known anti-tumor activities, creating dual targeting compounds. Molecular docking studies were conducted with the epidermal growth factor receptor (EGFR), to which the original peptide sequence is known to bind, as well as the estrogen receptor (ERα) and peroxisome proliferator-activated receptor (PPARα) using both Autodock Vina and FireDock. Based on docking results, peptide conjugates and peptides were selected and subjected to molecular dynamics simulations. MMGBSA calculations were used to further probe the binding energies. ADME studies revealed that the compounds were not CYP substrates, though most were Pgp substrates. Additionally, most of the peptides and conjugates showed MDCK permeability. Our results indicated that several of the peptide conjugates enhanced binding interactions with the receptors and resulted in stable receptor-ligand complexes; Furthermore, they may successfully target ERα and PPARα in addition to EGFR and may be further explored for synthesis and biological studies for therapeutic applications.

In-silico evaluation of bioactive compounds from tea as potential SARS-CoV-2 nonstructural protein 16 inhibitors

BACKGROUND AND AIM

A novel coronavirus, called the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been found to cause COVID-19 in humans and some other mammals. The nonstructural protein 16 (NSP16) of SARS-CoV-2 plays a significant part in the replication of viruses and suppresses the ability of innate immune system to detect the virus. Therefore, inhibiting NSP16 can be a secure path towards identifying a potent medication against SARS-CoV-2. Tea (Camellia sinensis) polyphenols have been reported to exhibit potential treatment options against various viral diseases.

METHODS

We conducted molecular docking and structural dynamics studies with a set of 65 Tea bioactive compounds to illustrate their ability to inhibit NSP16 of SARS-CoV-2. Moreover, post-simulations end state thermodynamic free energy calculations were estimated to strengthen our results.

RESULTS AND CONCLUSION

Six bioactive tea molecules showed better docking scores than the standard molecule sinefungin. These results were further validated by MD simulations, where Theaflavin compound demonstrated lower binding free energy in comparison to the standard molecule sinefungin. The compound theaflavin could be considered as a novel lead compound for further evaluation by in-vitro and in-vivo studies.

Predicting Membrane-Active Peptide Dynamics in Fluidic Lipid Membranes

Understanding the interactions between peptides and lipid membranes could not only accelerate the development of antimicrobial peptides as treatments for infections but also be applied to finding targeted therapies for cancer and other diseases. However, designing biophysical experiments to study molecular interactions between flexible peptides and fluidic lipid membranes has been an ongoing challenge. Recently, with hardware advances, algorithm improvements, and more accurate parameterizations (i.e., force fields), all-atom molecular dynamics (MD) simulations have been used as a "computational microscope" to investigate the molecular interactions and mechanisms of membrane-active peptides in cell membranes (Chen et al., Curr Opin Struct Biol 61:160-166, 2020; Ulmschneider and Ulmschneider, Acc Chem Res 51(5):1106-1116, 2018; Dror et al., Annu Rev Biophys 41:429-452, 2012). In this chapter, we describe how to utilize MD simulations to predict and study peptide dynamics and how to validate the simulations by circular dichroism, intrinsic fluorescent probe, membrane leakage assay, electrical impedance, and isothermal titration calorimetry. Experimentally validated MD simulations open a new route towards peptide design starting from sequence and structure and leading to desirable functions.

Structural homology between 11 beta-hydroxysteroid dehydrogenase and Mycobacterium tuberculosis Inh-A enzyme: Dehydroepiandrosterone as a potential co-adjuvant treatment in diabetes-tuberculosis comorbidity

Metabolic syndrome is considered the precursor of type 2 diabetes mellitus. Tuberculosis is a leading infection that constitutes a global threat remaining a major cause of morbi-mortality in developing countries. People with type 2 diabetes mellitus are more likely to suffer from infection with Mycobacterium tuberculosis. For both type 2 diabetes mellitus and tuberculosis, there is pulmonary production of anti-inflammatory glucocorticoids mediated by the enzyme 11β-hydroxysteroid dehydrogenase type 1 (11β-HSD1). The adrenal hormone dehydroepiandrosterone (DHEA) counteracts the glucocorticoid effects of cytokine production due to the inhibition of 11β-HSD1. Late advanced tuberculosis has been associated with the suppression of the Th1 response, evidenced by a high ratio of cortisol/DHEA. In a murine model of metabolic syndrome, we determined whether DHEA treatment modifies the pro-inflammatory cytokines due to the inhibition of the 11β-HSD1 expression. Since macrophages express 11β-HSD1, our second goal was incubating them with DHEA and Mycobacterium tuberculosis to show that the microbicide effect was increased by DHEA. Enoyl-acyl carrier protein reductase (InhA) is an essential enzyme of Mycobacterium tuberculosis involved in the mycolic acid synthesis. Because 11β-HSD1 and InhA are members of a short-chain dehydrogenase/reductase family of enzymes, we hypothesize that DHEA could be an antagonist of InhA. Our results demonstrate that DHEA has a direct microbicide effect against Mycobacterium tuberculosis; this effect was supported by in silico docking analysis and the molecular dynamic simulation studies between DHEA and InhA. Thus, DHEA increases the production of pro-inflammatory cytokines in the lung, inactivates GC by 11β-HSD1, and inhibits mycobacterial InhA. The multiple functions of DHEA suggest that this hormone or its synthetic analogs could be an efficient co-adjuvant for tuberculosis treatment.

Molecular docking and dynamics studies on propolis sulabiroin-A as a potential inhibitor of SARS-CoV-2

Molecular docking and dynamics simulations were conducted to investigate the antiviral activity of Propolis Sulabiroin-A to inhibit the SARS-CoV-2 virus with quercetin, hesperidin, and remdesivir as control ligands. The parameters calculated were docking score and binding energy/molecular mechanics-generalized born surface area (MMGBSA), root mean square displacement (RMSD), and root mean square fluctuation (RMSF). Docking and MMGBSA scores showed that all the ligands demonstrate an excellent candidate as an inhibitor, and the order of both scores is hesperidin, remdesivir, quercetin, and sulabiroin-A. The molecular dynamics simulation showed that all the ligands are good candidates as inhibitors. Although the fluctuation of Sulabiroin-A is relatively high, it has less protein-ligand interaction time than other ligands. Overall, there is still a good possibility that sulabiroin-A can be used as an alternative inhibitor if a new structure of receptor SARS-CoV-2 is used.

DrugHybrid_BS: Using Hybrid Feature Combined With Bagging-SVM to Predict Potentially Druggable Proteins

Drug targets are biological macromolecules or biomolecule structures capable of specifically binding a therapeutic effect with a particular drug or regulating physiological functions. Due to the important value and role of drug targets in recent years, the prediction of potential drug targets has become a research hotspot. The key to the research and development of modern new drugs is first to identify potential drug targets. In this paper, a new predictor, DrugHybrid_BS, is developed based on hybrid features and Bagging-SVM to identify potentially druggable proteins. This method combines the three features of monoDiKGap (k = 2), cross-covariance, and grouped amino acid composition. It removes redundant features and analyses key features through MRMD and MRMD2.0. The cross-validation results show that 96.9944% of the potentially druggable proteins can be accurately identified, and the accuracy of the independent test set has reached 96.5665%. This all means that DrugHybrid_BS has the potential to become a useful predictive tool for druggable proteins. In addition, the hybrid key features can identify 80.0343% of the potentially druggable proteins combined with Bagging-SVM, which indicates the significance of this part of the features for research.

Structure-Based Virtual Screening for Ligands of G Protein-Coupled Receptors: What Can Molecular Docking Do for You?

G protein-coupled receptors (GPCRs) constitute the largest family of membrane proteins in the human genome and are important therapeutic targets. During the last decade, the number of atomic-resolution structures of GPCRs has increased rapidly, providing insights into drug binding at the molecular level. These breakthroughs have created excitement regarding the potential of using structural information in ligand design and initiated a new era of rational drug discovery for GPCRs. The molecular docking method is now widely applied to model the three-dimensional structures of GPCR-ligand complexes and screen for chemical probes in large compound libraries. In this review article, we first summarize the current structural coverage of the GPCR superfamily and the understanding of receptor-ligand interactions at atomic resolution. We then present the general workflow of structure-based virtual screening and strategies to discover GPCR ligands in chemical libraries. We assess the state of the art of this research field by summarizing prospective applications of virtual screening based on experimental structures. Strategies to identify compounds with specific efficacy and selectivity profiles are discussed, illustrating the opportunities and limitations of the molecular docking method. Our overview shows that structure-based virtual screening can discover novel leads and will be essential in pursuing the next generation of GPCR drugs. SIGNIFICANCE STATEMENT: Extraordinary advances in the structural biology of G protein-coupled receptors have revealed the molecular details of ligand recognition by this large family of therapeutic targets, providing novel avenues for rational drug design. Structure-based docking is an efficient computational approach to identify novel chemical probes from large compound libraries, which has the potential to accelerate the development of drug candidates.

Dynophore-Based Approach in Virtual Screening: A Case of Human DNA Topoisomerase IIα

In this study, we utilized human DNA topoisomerase IIα as a model target to outline a dynophore-based approach to catalytic inhibitor design. Based on MD simulations of a known catalytic inhibitor and the native ATP ligand analog, AMP-PNP, we derived a joint dynophore model that supplements the static structure-based-pharmacophore information with a dynamic component. Subsequently, derived pharmacophore models were employed in a virtual screening campaign of a library of natural compounds. Experimental evaluation identified flavonoid compounds with promising topoisomerase IIα catalytic inhibition and binding studies confirmed interaction with the ATPase domain. We constructed a binding model through docking and extensively investigated it with molecular dynamics MD simulations, essential dynamics, and MM-GBSA free energy calculations, thus reconnecting the new results to the initial dynophore-based screening model. We not only demonstrate a new design strategy that incorporates a dynamic component of molecular recognition, but also highlight new derivates in the established flavonoid class of topoisomerase II inhibitors.

Screening S protein - ACE2 blockers from natural products: Strategies and advances in the discovery of potential inhibitors of COVID-19

The Coronavirus disease, 2019 (COVID-19) is caused by severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2), which poses a major threat to human life and health. Given its continued development, limiting the spread of COVID-19 in the population remains a challenging task. Currently, multiple therapies are being tried around the world to deal with SARS-CoV-2 infection, and a variety of studies have shown that natural products have a significant effect on COVID-19 patients. The combination of SARS-CoV-2 S protein with Angiotensin converting enzyme II(ACE2) of host cell to promote membrane fusion is an initial critical step for SARS-CoV-2 infection. Therefore, screening natural products that inhibit the binding of SARS-CoV-2 S protein and ACE2 also provides a feasible strategy for the treatment of COVID-19. Establishment of high throughput screening model is an important basis and key technology for screening S protein-ACE2 blockers. Based on this, the molecular structures of SARS-CoV-2 and ACE2 and their processes in the life cycle of SARS-CoV-2 and host cell infection were firstly reviewed in this paper, with emphasis on the methods and techniques of screening S protein-ACE2 blockers, including Virtual Screening (VS), Surface Plasmon Resonance (SPR), Biochromatography, Biotin-avidin with Enzyme-linked Immunosorbent assay and Gene Chip Technology. Furthermore, the technical principle, advantages and disadvantages and application scope were further elaborated. Combined with the application of the above screening technologies in S protein-ACE2 blockers, a variety of natural products, such as flavonoids, terpenoids, phenols, alkaloids, were summarized, which could be used as S protein-ACE2 blockers, in order to provide ideas for the efficient discovery of S protein-ACE2 blockers from natural sources and contribute to the development of broad-spectrum anti coronavirus drugs.

Appraisal of Bioactive Compounds of Betel Fruit as Antimalarial Agents by Targeting Plasmepsin 1 and 2: A Computational Approach

In many countries, the fruit of betel (Piper betle Linn) is traditionally used as medicine for treating malaria. It is a fatal disease, and existing medications are rapidly losing potency, necessitating the development of innovative pharmaceutics. The current study attempted to determine the compounds in the n-hexane fraction of betel fruit extract and investigate the potential inhibition of bioactive compounds against aspartic protease plasmepsin 1 (PDB ID: 3QS1) and plasmepsin 2 (PDB ID: 1LEE) of Plasmodium falciparum using a computational approach. The ethanol extract was fractionated into n-hexane and further analyzed using gas chromatography-mass spectrometry (GC-MS) to obtain information regarding the compounds contained in betel fruit. Each compound's potential antimalarial activity was evaluated using AutoDock Vina and compared to artemisinin, an antimalarial drug. Molecular dynamics simulations (MDSs) were performed to evaluate the stability of the interaction between the ligand and receptors. Results detected 20 probable compounds in the n-hexane extract of betel fruit based on GC-MS analysis. The docking study revealed that androstan-17-one,3-ethyl-3-hydroxy-, (5 alpha)- has the highest binding affinity for plasmepsin 1 and plasmepsin 2. The compound exhibits a similar interaction with artemisinin at the active site of the receptors. The compound does not violate Lipinski's rules of five. It belongs to class 5 toxicity with an LD₅₀ of 3000 mg/kg. MDS results showed stable interactions between the compound and the receptors. Our study concluded that androstan-17-one,3-ethyl-3-hydroxy-, (5 alpha)- from betel fruit has the potential to be further investigated as a potential inhibitor of the aspartic protease plasmepsin 1 and plasmepsin 2 of Plasmodium falciparum.

Exploring the Interactions of Ionic Liquids with Bio-Organic Amphiphiles Using Computational Approaches

Bio-organic amphiphiles have been shown to effectively impart unique physicochemical properties to ionic liquids resulting in the formation of versatile hybrid composites. In this work, we utilized computational methods to probe the formation and properties of hybrids prepared by mixing three newly designed bio-organic amphiphiles with 14 ionic liquids containing cholinium or glycine betaine cations and a variety of anions. The three amphiphiles were designed such that they contain unique biological moieties found in nature by conjugating (a) malic acid with the amino acid glutamine, (b) thiomalic acid with the antiviral, antibacterial pyrazole compound [3-(3,5-dimethyl-1H-pyrazol-1-yl)benzyl]amine, and (c) Fmoc-protected valine with diphenyl amine. Conductor-like screening model for real solvents (COSMO-RS) was used to obtain sigma profiles of the hybrid mixtures and to predict viscosities and mixing enthalpies of each composite. These results were used to determine optimal ionic liquid-bio-organic amphiphile mixtures. Molecular dynamics simulations of three optimal hybrids were then performed, and the interactions involved in the formation of the hybrids were analyzed. Our results indicated that cholinium-based ILs interacted most favorably with the amphiphiles through a variety of inter- and intramolecular interactions. This work serves to illustrate important factors that influence the interactions between bio-organic amphiphiles and bio-ILs and aids in the development of novel ionic liquid-based composites for a wide variety of potential biological applications.

Structural insights and molecular dynamics into the inhibitory mechanism of a Kunitz-type trypsin inhibitor from Tamarindus indica L

Trypsin inhibitors from tamarind seed have been studied in vitro and in preclinical studies for the treatment of obesity, its complications and associated comorbidities. It is still necessary to fully understand the structure and behaviour of these molecules. We purifed this inhibitor, sequenced de novo by MALDI-TOF/TOF, performed its homology modelling, and assessed the interaction with the trypsin enzyme through molecular dynamics (MD) simulation under physiological conditions. We identified additional 75 amino acid residues, reaching approximately 72% of total coverage. The four best conformations of the best homology modelling were submitted to the MD. The conformation n°287 was selected considering the RMSD analysis and interaction energy (-301.0128 kcal.mol-1). Residues Ile (54), Pro (57), Arg (59), Arg (63), and Glu (78) of pTTI presented the highest interactions with trypsin, and arginine residues were mainly involved in its binding mechanism. The results favour bioprospecting of this protein for pharmaceutical health applications.

Molecular Modeling Techniques Applied to the Design of Multitarget Drugs: Methods and Applications

Multifactorial diseases, such as cancer and diabetes present a challenge for the traditional "one-target, one disease" paradigm due to their complex pathogenic mechanisms. Although a combination of drugs can be used, a multitarget drug may be a better choice face of its efficacy, lower adverse effects and lower chance of resistance development. The computer-based design of these multitarget drugs can explore the same techniques used for single-target drug design, but the difficulties associated to the obtention of drugs that are capable of modulating two or more targets with similar efficacy impose new challenges, whose solutions involve the adaptation of known techniques and also to the development of new ones, including machine-learning approaches. In this review, some SBDD and LBDD techniques for the multitarget drug design are discussed, together with some cases where the application of such techniques led to effective multitarget ligands.

Paratope states in solution improve structure prediction and docking

Structure-based antibody design and accurate predictions of antibody-antigen interactions remain major challenges in computational biology. By using molecular dynamics simulations, we show that a single static X-ray structure is not sufficient to identify determinants of antibody-antigen recognition. Here, we investigate antibodies that undergo substantial conformational changes upon antigen binding and have been classified as difficult cases in an extensive benchmark for antibody-antigen docking. We present thermodynamics and transition kinetics of these conformational rearrangements and show that paratope states can be used to improve antibody-antigen docking. By using the unbound antibody X-ray structure as starting structure for molecular dynamics simulations, we retain a binding competent conformation substantially different to the unbound antibody X-ray structure. We also observe that the kinetically dominant antibody paratope conformations are chosen by the bound antigen conformation with the highest probability. Thus, we show that paratope states in solution can improve antibody-antigen docking and structure prediction.

Investigation of the interaction between Chrysoeriol and xanthine oxidase using computational and in vitro approaches

Xanthine oxidase (XO) plays a vital role in inducing hyperuricemia and increasing the level of superoxide free radicals in blood, and is proved as an important target for gout. Chrysoeriol (CHE) is a natural flavone with potent XO inhibitory activity (IC₅₀ = 2.487 ± 0.213 μM), however, the mechanism of interaction is still unclear. Therefore, a comprehensive analysis of the interaction between CHE and XO was accomplished by enzyme kinetics, isothermal titration calorimetry (ITC), multi-spectroscopic methods, molecular simulation and ADMET. The results showed that CHE acted as a rapid reversible and competitive-type XO inhibitor and its binding to XO was driven by hydrogen bonding and hydrophobic interaction. Moreover, CHE exhibited a strong fluorescence quenching effect through a static quenching procedure and induced conformational changes of XO. Its binding pattern with XO was revealed by docking study and the binding affinity to XO was enhanced by the interactions with key amino acid residues in the active pocket of XO. Further, CHE showed good stability and pharmacokinetic behavior properties in molecule dynamic simulation and ADMET prediction. Overall, this study shed some light on the mechanism of interaction between CHE and XO, also provided some valuable information concerning the future therapeutic application of CHE as natural XO inhibitor.

Nonantimicrobial Actions of Macrolides: Overview and Perspectives for Future Development

Macrolides are among the most widely prescribed broad spectrum antibacterials, particularly for respiratory infections. It is now recognized that these drugs, in particular azithromycin, also exert time-dependent immunomodulatory actions that contribute to their therapeutic benefit in both infectious and other chronic inflammatory diseases. Their increased chronic use in airway inflammation and, more recently, of azithromycin in COVID-19, however, has led to a rise in bacterial resistance. An additional crucial aspect of chronic airway inflammation, such as chronic obstructive pulmonary disease, as well as other inflammatory disorders, is the loss of epithelial barrier protection against pathogens and pollutants. In recent years, azithromycin has been shown with time to enhance the barrier properties of airway epithelial cells, an action that makes an important contribution to its therapeutic efficacy. In this article, we review the background and evidence for various immunomodulatory and time-dependent actions of macrolides on inflammatory processes and on the epithelium and highlight novel nonantibacterial macrolides that are being studied for immunomodulatory and barrier-strengthening properties to circumvent the risk of bacterial resistance that occurs with macrolide antibacterials. We also briefly review the clinical effects of macrolides in respiratory and other inflammatory diseases associated with epithelial injury and propose that the beneficial epithelial effects of nonantibacterial azithromycin derivatives in chronic inflammation, even given prophylactically, are likely to gain increasing attention in the future. SIGNIFICANCE STATEMENT: Based on its immunomodulatory properties and ability to enhance the protective role of the lung epithelium against pathogens, azithromycin has proven superior to other macrolides in treating chronic respiratory inflammation. A nonantibiotic azithromycin derivative is likely to offer prophylactic benefits against inflammation and epithelial damage of differing causes while preserving the use of macrolides as antibiotics.