Software for molecular docking: a review

Al-Humaidi J, Zaki MEA, Fathalla M, Gomha SM. Mechanochemical Synthesis and Molecular Docking Studies of New Azines Bearing Indole as Anticancer Agents

The development of new approaches for the synthesis of new bioactive heterocyclic derivatives is of the utmost importance for pharmaceutical industry. In this regard, the present study reports the green synthesis of new benzaldazine and ketazine derivatives via the condensation of various carbonyl compounds (aldehydes and ketones with the 3-(1-hydrazineylideneethyl)-1H-indole using the grinding method with one drop of acetic acid). Various spectroscopic techniques were used to identify the structures of the synthesized derivatives. Furthermore, the anticancer activities of the reported azine derivatives were evaluated against colon, hepatocellular, and breast carcinoma cell lines using the MTT technique with doxorubicin as a reference medication. The findings suggested that the synthesized derivatives exhibited potential anti-tumor activities toward different cell lines. For example, 3c, 3d, 3h, 9, and 13 exhibited interesting activity with an IC50 value of 4.27-8.15 µM towards the HCT-116 cell line as compared to doxorubicin (IC50 = 5.23 ± 0.29 µM). In addition, 3c, 3d, 3h, 9, 11, and 13 showed excellent cytotoxic activities (IC50 = 4.09-9.05 µM) towards the HePG-2 cell line compared to doxorubicin (IC50 = 4.50 ± 0.20 µM), and 3d, 3h, 9, and 13 demonstrated high potency (IC50 = 6.19-8.39 µM) towards the breast cell line (MCF-7) as compared to the reference drug (IC50 = 4.17 ± 0.20 µM). The molecular interactions between derivatives 3a-h, 7, 9, 11, 13, and the CDK-5 enzyme (PDB ID: 3IG7) were studied further using molecular docking indicating a high level of support for the experimental results. Furthermore, the drug-likeness analysis of the reported derivatives indicated that derivative 9 (binding affinity = -8.34 kcal/mol) would have a better pharmacokinetics, drug-likeness, and oral bioavailability as compared to doxorubicin (-7.04 kcal/mol). These results along with the structure-activity relationship (SAR) of the reported derivatives will pave the way for the design of additional azines bearing indole with potential anticancer activities.

Transporter proteins knowledge graph construction and its application in drug development

Transporters are the main determinant for pharmacokinetics characteristics of drugs, such as absorption, distribution, and excretion of drugs in humans. However, it is difficult to perform drug transporter validation and structure analysis of membrane transporter proteins by experimental methods. Many studies have demonstrated that knowledge graphs (KG) could effectively excavate potential association information between different entities. To improve the effectiveness of drug discovery, a transporter-related KG was constructed in this study. Meanwhile, a predictive frame (AutoInt_KG) and a generative frame (MolGPT_KG) were established based on the heterogeneity information obtained from the transporter-related KG by the RESCAL model. Natural product Luteolin with known transporters was selected to verify the reliability of the AutoInt_KG frame, its ROC-AUC (1:1), ROC-AUC (1:10), PR-AUC (1:1), PR-AUC (1:10) are 0.91, 0.94, 0.91 and 0.78, respectively. Subsequently, the MolGPT_KG frame was constructed to implement efficient drug design based on transporter structure. The evaluation results showed that the MolGPT_KG could generate novel and valid molecules and that these molecules were further confirmed by molecular docking analysis. The docking results showed that they could bind to important amino acids at the active site of the target transporter. Our findings will provide rich information resources and guidance for the further development of the transporter-related drugs.

MELD-Bracket Ranks Binding Affinities of Diverse Sets of Ligands

Affinity ranking of structurally diverse small-molecule ligands is a challenging problem with important applications in structure-based drug discovery. Absolute binding free energy methods can model diverse ligands, but the high computational cost of the current methods limits application to data sets with few ligands. We recently developed MELD-Bracket, a Molecular Dynamics method for efficient affinity ranking of ligands [ JCTC 2022, 18 (1), 374-379]. It utilizes a Bayesian framework to guide sampling to relevant regions of phase space, and it couples this with a bracket-like competition on a pool of ligands. Here we find that 6-competitor MELD-Bracket can rank dozens of diverse ligands that have low structural similarity and different net charges. We benchmark it on four protein systems─PTB1B, Tyk2, BACE, and JAK3─having varied modes of interactions. We also validated 8-competitor and 12-competitor protocols. The MELD-Bracket protocols presented here may have the appropriate balance of accuracy and computational efficiency to be suitable for ranking diverse ligands from typical drug discovery campaigns.

Virtual Screening Strategy to Identify Retinoic Acid-Related Orphan Receptor γt Modulators

Molecular docking is a key method used in virtual screening (VS) campaigns to identify small-molecule ligands for drug discovery targets. While docking provides a tangible way to understand and predict the protein-ligand complex formation, the docking algorithms are often unable to separate active ligands from inactive molecules in practical VS usage. Here, a novel docking and shape-focused pharmacophore VS protocol is demonstrated for facilitating effective hit discovery using retinoic acid receptor-related orphan receptor gamma t (RORγt) as a case study. RORγt is a prospective target for treating inflammatory diseases such as psoriasis and multiple sclerosis. First, a commercial molecular database was flexibly docked. Second, the alternative docking poses were rescored against the shape/electrostatic potential of negative image-based (NIB) models that mirror the target's binding cavity. The compositions of the NIB models were optimized via iterative trimming and benchmarking using a greedy search-driven algorithm or brute force NIB optimization. Third, a pharmacophore point-based filtering was performed to focus the hit identification on the known RORγt activity hotspots. Fourth, free energy binding affinity evaluation was performed on the remaining molecules. Finally, twenty-eight compounds were selected for in vitro testing and eight compounds were determined to be low μM range RORγt inhibitors, thereby showing that the introduced VS protocol generated an effective hit rate of ~29%.

Multivariate image analysis applied to quantitative structure-activity relationships and docking studies of recent hydroxyphenylpyruvate deoxygenase inhibitors

BACKGROUND

Mesotrione is a triketone widely used as an inhibitor of the hydroxyphenylpyruvate deoxygenase (HPPD) enzyme. However, new agrochemicals should be developed continuously to tackle the problem of herbicide resistance. Two sets of mesotrione analogs have been synthesized recently and they have demonstrated successful phytotoxicity against weeds. In this study, these compounds were joined to form a single data set and the HPPD inhibition of this enlarged library of triketones was modeled using multivariate image analysis applied to quantitative structure-activity relationships (MIA-QSAR). Docking studies were also carried out to validate the MIA-QSAR findings and to aid the interpretation of ligand-enzyme interactions responsible for the bioactivity (pIC₅₀ ).

RESULTS

The MIA-QSAR models based on van der Waals radii (r_vdW ), electronegativity (ε), and the r_vdW /ε ratio as molecular descriptors were both predictive to an acceptable degree (r²  ≥ 0.80, q²  ≥ 0.68 and r² _pred  ≥ 0.68). Subsequently, partial least squares (PLS) regression parameters were applied to predict the pIC₅₀ values of newly proposed derivatives, yielding a few promising agrochemical candidates. The calculated log P for most of these derivatives was found to be higher than that of mesotrione and the library compounds, indicating that they should be less prone to leach out and contaminate groundwater.

CONCLUSION

Multivariate image analysis descriptors corroborated by docking studies were capable of modeling the herbicidal activities of 68 triketones reliably. Due to the substituent effects at the triketone framework, particularly of a nitro group in R³ , promising analogs could be designed. The P9 proposal demonstrated higher calculated activity and log P than commercial mesotrione. © 2023 Society of Chemical Industry.

Predicting allosteric sites using fast conformational sampling as guided by coarse-grained normal modes

To computationally identify cryptic binding sites for allosteric modulators, we have developed a fast and simple conformational sampling scheme guided by coarse-grained normal modes solved from the elastic network models followed by atomistic backbone and sidechain reconstruction. Despite the complexity of conformational changes associated with ligand binding, we previously showed that simply sampling along each of the lowest 30 modes can adequately restructure cryptic sites so they are detectable by pocket finding programs like Concavity. Here, we applied this method to study four classical examples of allosteric regulation (GluR2 receptor, GroEL chaperonin, GPCR, and myosin). Our method along with alternative methods has been utilized to locate known allosteric sites and predict new promising allosteric sites. Compared with other sampling methods based on extensive molecular dynamics simulation, our method is both faster (1-2 h for an average-size protein of ∼400 residues) and more flexible (it can be easily integrated with any structure-based pocket finding methods), so it is suitable for high-throughput screening of large datasets of protein structures at the genome scale.

Dockey: a modern integrated tool for large-scale molecular docking and virtual screening

Molecular docking is a structure-based and computer-aided drug design approach that plays a pivotal role in drug discovery and pharmaceutical research. AutoDock is the most widely used molecular docking tool for study of protein-ligand interactions and virtual screening. Although many tools have been developed to streamline and automate the AutoDock docking pipeline, some of them still use outdated graphical user interfaces and have not been updated for a long time. Meanwhile, some of them lack cross-platform compatibility and evaluation metrics for screening lead compound candidates. To overcome these limitations, we have developed Dockey, a flexible and intuitive graphical interface tool with seamless integration of several useful tools, which implements a complete docking pipeline covering molecular sanitization, molecular preparation, paralleled docking execution, interaction detection and conformation visualization. Specifically, Dockey can detect the non-covalent interactions between small molecules and proteins and perform cross-docking between multiple receptors and ligands. It has the capacity to automatically dock thousands of ligands to multiple receptors and analyze the corresponding docking results in parallel. All the generated data will be kept in a project file that can be shared between any systems and computers with the pre-installation of Dockey. We anticipate that these unique characteristics will make it attractive for researchers to conduct large-scale molecular docking without complicated operations, particularly for beginners. Dockey is implemented in Python and freely available at https://github.com/lmdu/dockey.

Pharmacological Chaperones and Protein Conformational Diseases: Approaches of Computational Structural Biology

Whenever a protein fails to fold into its native structure, a profound detrimental effect is likely to occur, and a disease is often developed. Protein conformational disorders arise when proteins adopt abnormal conformations due to a pathological gene variant that turns into gain/loss of function or improper localization/degradation. Pharmacological chaperones are small molecules restoring the correct folding of a protein suitable for treating conformational diseases. Small molecules like these bind poorly folded proteins similarly to physiological chaperones, bridging non-covalent interactions (hydrogen bonds, electrostatic interactions, and van der Waals contacts) loosened or lost due to mutations. Pharmacological chaperone development involves, among other things, structural biology investigation of the target protein and its misfolding and refolding. Such research can take advantage of computational methods at many stages. Here, we present an up-to-date review of the computational structural biology tools and approaches regarding protein stability evaluation, binding pocket discovery and druggability, drug repurposing, and virtual ligand screening. The tools are presented as organized in an ideal workflow oriented at pharmacological chaperones' rational design, also with the treatment of rare diseases in mind.

Molecular docking of 5-o-benzoylpinostrobin derivatives from boesenbergia pandurata roxb. as anti-inflammatory

Background: The use of NSAIDs, also known as non-steroidal anti-inflammatory drugs, has numerous adverse effects and consequences. For this reason, it is necessary to develop rational drugs as safer anti-inflammatory drugs with fewer side effects. Temu Kunci rhizome contains Pinostrobin (5-hydroxy-7- methoxyflavanone), which is believed to have anti-inflammatory properties. Objective: This study aims to determine the strongest anti-inflammatory activity at the cyclooxygenase-2 (COX-2) receptor through the 5-O-Benzoylpinostrobin derivative design. Methods: AutoDockTools on the COX-2 receptor (PDB code: 5IKR) were used in molecular docking in this study. The metrics employed were binding afinity (ΔG), inhibition constant (Ki), which serve as indicators of affinities, and amino acid residue similarity, which serves as a measure of the similarity of interactions. Predictive scores were confirmed by Molecular Docking Simulation. Results: The top five 5-O-Benzoylpinostrobin derivatives show a high affinity for the COX-2 receptor compared to Pinostrobin as a marker compound of Boesenbergia pandurata Roxb and furthermore give the lowest inhibition constant (Ki) and the highest negative binding free energy (ΔG), 35.40, 45.21, 54.75, 64.43, 76.97 nM and -10.16, -10.02, -9.91, -9.81, -9.7 kcal/mol. Interestingly, the five 5-O-Benzoylpinostrobin derivatives also have higher affinity than the native ligand Mefenamic acid, which is known to be a non-selective COX-2 inhibitor. The highest predicted affinity was shown by 4-Nitro-5-O-benzoylpinostrobin for the COX-2 receptor (PDP ID: 5IKR), with a higher predicted affinity for Mefenamic acid. Conclusion: The five selected 5-O-Benzoylpinostrobin derivatives were potent modifications of pinostrobin as an anti-inflammatory because they showed a higher affinity than Pinostrobin and Mefenamic acid. This study demonstrated that it is highly feasible to produce and test the novel 5-O-Benzoylpinostrobin derivative in vivo, specifically 4-Nitro-5-O-benzoylpinostrobin.

Identificación de inhibidores de las enzimas RdRp y Mpro del virus SARS-CoV-2 mediante homología estructural

El COVID-19 ha generado un enorme impacto en la salud pública mundial debido a las altas tasas de contagio y mortalidad asociadas al virus SARS-CoV-2 causante de la enfermedad. Hasta la fecha, la Organización Mundial de la Salud (OMS) ha aprobado el uso de 10 vacunas aparentemente seguras y eficaces. Sin embargo, todavía existen limitaciones importantes para su administración en países en vías de desarrollo y localidades remotas, y la preocupación por la aparición de variantes del virus que puedan evadir la inmunidad adquirida mediante la vacunación se mantiene latente. Además de la prevención de la infección, son necesarios agentes terapéuticos efectivos para tratar a los pacientes diagnosticados con COVID-19. Bajo este contexto, el presente estudio tuvo como objetivo realizar un cribado virtual basado en la estructura de las enzimas proteasa (Mpro) y ARN polimerasa ARN-dependiente (RdRp) del SARS-CoV-2. Para este propósito se ensayaron inhibidores de proteínas homólogas pertenecientes a diferentes virus. El alineamiento múltiple de secuencias de estas enzimas permitió reconocer la presencia de una alta conservación de estas enzimas entre especies, especialmente de las regiones que comprenden los sitios de unión a inhibidores. Por lo tanto, se deduce que es posible emplear un enfoque de redireccionamiento de los inhibidores que fueron diseñados para tratar otras enfermedades virales. Experimentos de acoplamiento molecular permitieron identificar que los inhibidores RTP (afinidad de unión = -7.3 kcal/mol) y V3D (afinidad de unión = -8.0 kcal/mol) son excelentes inhibidores de RdRp y Mpro, respectivamente. Estos resultados sugieren que dichas moléculas son virtualmente capaces de unirse e inhibir la actividad de RdRp y Mpro y por lo tanto constituyen potenciales fármacos para combatir el SARS-CoV-2. Palabras clave: SARS-CoV-2, COVID-19, inhibidores, RdRp, Mpro.

Deep Learning Model for Efficient Protein-Ligand Docking with Implicit Side-Chain Flexibility

Protein-ligand docking is an essential tool in structure-based drug design with applications ranging from virtual high-throughput screening to pose prediction for lead optimization. Most docking programs for pose prediction are optimized for redocking to an existing cocrystallized protein structure, ignoring protein flexibility. In real-world drug design applications, however, protein flexibility is an essential feature of the ligand-binding process. Flexible protein-ligand docking still remains a significant challenge to computational drug design. To target this challenge, we present a deep learning (DL) model for flexible protein-ligand docking based on the prediction of an intermolecular Euclidean distance matrix (EDM), making the typical use of iterative search algorithms obsolete. The model was trained on a large-scale data set of protein-ligand complexes and evaluated on independent test sets. Our model generates high quality poses for a diverse set of protein and ligand structures and outperforms comparable docking methods.

Molecular modeling of pyrrolo-pyrimidine based analogs as potential FGFR1 inhibitors: a scientific approach for therapeutic drugs

Fibroblast growth factor receptors 1 (FGFR1) is an emerging target for the development of anticancer drugs. Uncontrolled expression of FGFR1 is strongly associated with a number of different types of cancers. Apart from a few FGFR inhibitors, the FGFR family members have not been thoroughly studied to produce clinically effective anticancer drugs. The application of proper computational techniques may aid in understanding the mechanism of protein-ligand complex formation, which may provide a better notion for developing potent FGFR1 inhibitors. In this study, a variety of computational techniques, including 3D-QSAR, flexible docking and MD simulation followed by MMGB/PBSA, H-bonds and distance analysis, have been performed to systematically explore the binding mechanism of pyrrolo-pyrimidine derivatives against FGFR1. The 3D-QSAR model was generated to deduce the structural determinants of FGFR1 inhibition. The high q2 and r2 values for the CoMFA and CoMSIA models indicated that the created 3D-QSAR models could reliably predict the bioactivities of FGFR1 inhibitors. The computed binding free energies (MMGB/PBSA) for the selected compounds were consistent with the ranking of their experimental binding affinities against FGFR1. Furthermore, per-residue energy decomposition analysis revealed that the residues Lys514 in catalytic region, Asn568, Glu571 in solvent accessible portion and Asp641 in DFG motif exhibited a strong tendency to mediate ligand-protein interactions through the hydrogen bonding and Van Der Waals interactions. These findings may benefit researchers in gaining better knowledge of FGFR1 inhibition and may serve as a guideline for the development of novel and highly effective FGFR1 inhibitors.Communicated by Ramaswamy H. Sarma.

Study of two combined series of triketones with HPPD inhibitory activity by molecular modelling

Triketones are suitable compounds for 4-hydroxyphenylpyruvate dioxygenase (HPPD) inhibition and are important compounds for eliminating agricultural weeds. We report herein quantitative structure-activity relationship (QSAR) modelling and docking studies for a series of triketone-quinoline hybrids and 2-(aryloxyacetyl)cyclohexane-1,3-diones with the aim of proposing new chemical candidates that exhibit improved performance as herbicides. The QSAR models obtained were reliable and predictive (average r², q², and r²_pred of 0.72, 0.51, and 0.71, respectively). Guided by multivariate image analysis of the PLS regression coefficients and variable importance in projection scores, the substituent effects could be analysed, and a promising derivative with R¹ = H, R² = CN, and R³ = 5,7,8-triCl at the triketone-quinoline scaffold (P18) was proposed. Docking studies demonstrated that π-π stacking interactions and specific interactions between the substituents and amino acid residues in the binding site of the Arabidopsis thaliana HPPD (AtHPPD) enzyme support the desired bioactivity. In addition, compared to a benchmark commercial triketone (mesotrione), the proposed compounds are more lipophilic and less mobile in soil rich in organic matter and are less prone to contaminate groundwater.

Purification, identification and hypolipidemic activities of three novel hypolipidemic peptides from tea protein

In this study, hypolipidemic peptides were obtained from tea protein by enzymatic hydrolysis, ultrafiltration and high-performance liquid chromatography. Subsequently, the hypolipidemic peptides were identified by mass spectrometry and screened through molecular docking technology, and the hypolipidemic activities and mechanisms of the active peptides were explored. The results showed that the hydrolysate of hypolipidemic peptides obtained by pepsin hydrolysis for 3 h had good bile salt binding ability. After purification, identification and molecular docking screening, three novel hypolipidemic peptides FLF, IYF and QIF were obtained. FLF, IYF and QIF can interact with the receptor proteins 1LPB and 1F6W through hydrogen bonds, π-π bonds, hydrophobic interactions and van der Waals forces, thus exerting hypolipidemic activities. Activity studies showed that, compared with the positive controls, FLF, IYF and QIF had excellent sodium taurocholate binding abilities, pancreatic lipase inhibitory activities and cholesterol esterase inhibitory activities. Moreover, FLF, IYF and QIF can effectively inhibit lipogenic differentiation of 3T3-L1 preadipocytes, reduce intracellular lipid and low-density lipoprotein content and increase high-density lipoprotein content. These results indicated that the three novel hypolipidemic peptides screened in this study had excellent hypolipidemic activities and were expected to be used as natural-derived hypolipidemic active ingredients for the development and application in functional foods.

Molecular dynamics-derived pharmacophores of Schistosoma glutathione transferase in complex with bromosulfophthalein: Screening and analysis of potential inhibitors

Schistosoma glutathione transferases (GSTs) have been identified as attractive drug targets for the design of novel antischistosomals. Here, we used in silico methods to validate the discriminative inhibitory properties of bromosulfophthalein (BSP) against the 26-kDa GST from S. japonicum (Sj26GST), and the 28-kDa GST from S. haematobium (Sh28GST), versus human GST (hGST) isoforms alpha (hGSTA), mu (hGSTM) and pi (hGSTP). The use of BSP as an archetypal selective inhibitor was harnessed to produce molecular dynamics-derived pharmacophores of the two targets. Pharmacophore-based screening using a large dataset of experimental and approved drug compounds was performed to produce a shortlist of candidates. The top candidate for each target was prioritised via molecular docking, yielding guanosine-3'-monophosphate-5'-diphosphate (G3D) for Sj26GST, and quercetin-3'-O-phosphate (Q3P) for Sh28GST. Comparative molecular dynamics studies of both candidates compared to BSP showed similar characteristics of binding stability and strength, suggesting their potential to emulate the inhibitory effects of BSP.

In silico analysis prediction of HepTH1-5 as a potential therapeutic agent by targeting tumour suppressor protein networks

Many studies reported that the activation of tumour suppressor protein, p53 induced the human hepcidin expression. However, its expression decreased when p53 was silenced in human hepatoma cells. Contrary to Tilapia hepcidin TH1-5, HepTH1-5 was previously reported to trigger the p53 activation through the molecular docking approach. The INhibitor of Growth (ING) family members are also shown to directly interact with p53 and promote cell cycle arrest, senescence, apoptosis and participate in DNA replication and DNA damage responses to suppress the tumour initiation and progression. However, the interrelation between INGs and HepTH1-5 remains unknown. Therefore, this study aims to identify the mechanism and their protein interactions using in silico approaches. The finding revealed that HepTH1-5 and its ligands had interacted mostly on hotspot residues of ING proteins which involved in histone modifications via acetylation, phosphorylation, and methylation. This proves that HepTH1-5 might implicate in an apoptosis signalling pathway and preserve the protein structure and function of INGs by reducing the perturbation of histone binding upon oxidative stress response. This study would provide theoretical guidance for the design and experimental studies to decipher the role of HepTH1-5 as a potential therapeutic agent for cancer therapy. Communicated by Ramaswamy H. Sarma.

Advances in the discovery of new chemotypes through ultra-large library docking

INTRODUCTION

The size and complexity of virtual screening libraries in drug discovery have skyrocketed in recent years, reaching up to multiple billions of accessible compounds. However, virtual screening of such ultra-large libraries poses several challenges associated with preparing the libraries, sampling, and pre-selection of suitable compounds. The utilization of artificial intelligence (AI)-assisted screening approaches, such as deep learning, poses a promising countermeasure to deal with this rapidly expanding chemical space. For example, various AI-driven methods were recently successfully used to identify novel small molecule inhibitors of the SARS-CoV-2 main protease (M^pro).

AREAS COVERED

This review focuses on presenting various kinds of virtual screening methods suitable for dealing with ultra-large libraries. Challenges associated with these computational methodologies are discussed, and recent advances are highlighted in the example of the discovery of novel M^pro inhibitors targeting the SARS-CoV-2 virus.

EXPERT OPINION

With the rapid expansion of the virtual chemical space, the methodologies for docking and screening such quantities of molecules need to keep pace. Employment of AI-driven screening compounds has already been shown to be effective in a range from a few thousand to multiple billion compounds, furthered by de novo generation of drug-like molecules without human interference.

Application of computational methods for class A GPCR Ligand discovery

G protein-coupled receptors (GPCR) are integral membrane proteins of considerable interest as targets for drug development due to their role in transmitting cellular signals in a multitude of biological processes. Of the six classes categorizing GPCR (A, B, C, D, E, and F), class A contains the largest number of therapeutically relevant GPCR. Despite their importance as drug targets, many challenges exist for the discovery of novel class A GPCR ligands serving as drug precursors. Though knowledge of the structural and functional characteristics of GPCR has grown significantly over the past 20 years, a large portion of GPCR lack reported, experimentally determined structures. Furthermore, many GPCR have no known endogenous and/or synthetic ligands, limiting further exploration of their biochemical, cellular, and physiological roles. While many successes in GPCR ligand discovery have resulted from experimental high-throughput screening, computational methods have played an increasingly important role in GPCR ligand identification in the past decade. Here we discuss computational techniques applied to GPCR ligand discovery. This review summarizes class A GPCR structure/function and provides an overview of many obstacles currently faced in GPCR ligand discovery. Furthermore, we discuss applications and recent successes of computational techniques used to predict GPCR structure as well as present a summary of ligand- and structure-based methods used to identify potential GPCR ligands. Finally, we discuss computational hit list generation and refinement and provide comprehensive workflows for GPCR ligand identification.

Molecular Docking and In Vitro Studies of Ochratoxin A (OTA) Biodetoxification Testing Three Endopeptidases

Ochratoxin A (OTA) is considered one of the main mycotoxins responsible for health problems and considerable economic losses in the feed industry. The aim was to study OTA's detoxifying potential of commercial protease enzymes: (i) Ananas comosus bromelain cysteine-protease, (ii) bovine trypsin serine-protease and (iii) Bacillus subtilis neutral metalloendopeptidase. In silico studies were performed with reference ligands and T-2 toxin as control, and in vitro experiments. In silico study results showed that tested toxins interacted near the catalytic triad, similar to how the reference ligands behave in all tested proteases. Likewise, based on the proximity of the amino acids in the most stable poses, the chemical reaction mechanisms for the transformation of OTA were proposed. In vitro experiments showed that while bromelain reduced OTA's concentration in 7.64% at pH 4.6; trypsin at 10.69% and the neutral metalloendopeptidase in 8.2%, 14.44%, 45.26% at pH 4.6, 5 and 7, respectively (p < 0.05). The less harmful α-ochratoxin was confirmed with trypsin and the metalloendopeptidase. This study is the first attempt to demonstrate that: (i) bromelain and trypsin can hydrolyse OTA in acidic pH conditions with low efficiency and (ii) the metalloendopeptidase was an effective OTA bio-detoxifier. This study confirmed α-ochratoxin as a final product of the enzymatic reactions in real-time practical information on OTA degradation rate, since in vitro experiments simulated the time that food spends in poultry intestines, as well as their natural pH and temperature conditions.

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.

MetaDOCK: A Combinatorial Molecular Docking Approach

Molecular docking plays a major role in academic and industrial drug screening and discovery processes. Despite the availability of numerous docking software packages, there is a lot of scope for improvement for the docking algorithms in terms of becoming more reliable to replicate the experimental binding results. Here, we propose a combinatorial or consensus docking approach where complementary powers of the existing methods are captured. We created a meta-docking protocol by combining the results of AutoDock4.2, LeDock, and rDOCK programs as these are freely available, easy to use, and suitable for large-scale analysis and produced better performance on benchmarking studies. Rigorous benchmarking analyses were undertaken to evaluate the scoring, posing, and screening capability of our approach. Further, the performance measures were compared against one standard state-of-the-art commercial docking software, GOLD, and one freely available software, PLANTS. Performances of MetaDOCK for scoring, posing, and screening the protein-ligand complexes were found to be quite superior compared to the reference programs. Exhaustive molecular dynamics simulation and molecular mechanics Poisson-Boltzmann and surface area-based free energy estimation also suggest better energetic stability of the docking solutions produced by our meta-approach. We believe that the MetaDOCK approach is a useful packaging of the freely available software and provides a better alternative to the scientific community who are unable to afford costly commercial packages.

Benzimidazole Derivatives Suppress Fusarium Wilt Disease via Interaction with ERG6 of Fusarium equiseti and Activation of the Antioxidant Defense System of Pepper Plants

Sweet pepper (Capsicum annuum L.), also known as bell pepper, is one of the most widely grown vegetable crops worldwide. It is attacked by numerous phytopathogenic fungi, such as Fusarium equiseti, the causal agent of Fusarium wilt disease. In the current study, we proposed two benzimidazole derivatives, including 2-(2-hydroxyphenyl)-1-H benzimidazole (HPBI) and its aluminum complex (Al-HPBI complex), as potential control alternatives to F. equiseti. Our findings showed that both compounds demonstrated dose-dependent antifungal activity against F. equiseti in vitro and significantly suppressed disease development in pepper plants under greenhouse conditions. According to in silico analysis, the F. equiseti genome possesses a predicted Sterol 24-C-methyltransferase (FeEGR6) protein that shares a high degree of homology with EGR6 from F. oxysporum (FoEGR6). It is worth mentioning that molecular docking analysis confirmed that both compounds can interact with FeEGR6 from F. equiseti as well as FoEGR6 from F. oxysporum. Moreover, root application of HPBI and its aluminum complex significantly enhanced the enzymatic activities of guaiacol-dependent peroxidases (POX), polyphenol oxidase (PPO), and upregulated four antioxidant-related enzymes, including superoxide dismutase [Cu-Zn] (CaSOD-Cu), L-ascorbate peroxidase 1, cytosolic (CaAPX), glutathione reductase, chloroplastic (CaGR), and monodehydroascorbate reductase (CaMDHAR). Additionally, both benzimidazole derivatives induced the accumulation of total soluble phenolics and total soluble flavonoids. Collectively, these findings suggest that the application of HPBI and Al-HPBI complex induce both enzymatic and nonenzymatic antioxidant defense machinery.

Nude and Modified Electrospun Nanofibers, Application to Air Purification

Air transports several pollutants, including particulate matter (PM), which can produce cardiovascular and respiratory diseases. Thus, it is a challenge to control pollutant emissions before releasing them to the environment. Until now, filtration has been the most efficient processes for removing PM. Therefore, the electrospinning procedure has been applied to obtain membranes with a high filtration efficiency and low pressure drop. This review addressed the synthesis of polymers that are used for fabricating high-performance membranes by electrospinning to remove air pollutants. Then, the most influential parameters to produce electrospun membranes are indicated. The main results show that electrospun membranes are an excellent alternative to having air filters due to the versatility of the process, the capacity for controlling the fiber diameter, porosity, high filtration efficiency and low-pressure drop.

Docking-based generative approaches in the search for new drug candidates

Despite the popularity of virtual screening (VS) of existing compound libraries, the search for new potential drug candidates also takes advantage of generative protocols, where new compound suggestions are enumerated using various algorithms. To increase the activity potency of generative approaches, they have recently been coupled with molecular docking, a leading methodology of structure-based drug design (SBDD). In this review, we summarize progress since docking-based generative models emerged. We propose a new taxonomy for these methods and discuss their importance for the field of computer-aided drug design (CADD). In addition, we discuss the most promising directions for the further development of generative protocols coupled with docking.

Discovery of new STAT3 inhibitors as anticancer agents using ligand-receptor contact fingerprints and docking-augmented machine learning

STAT3 belongs to a family of seven vital transcription factors. High levels of STAT3 are detected in several types of cancer. Hence, STAT3 inhibition is considered a promising therapeutic anti-cancer strategy. In this work, we used multiple docked poses of STAT3 inhibitors to augment training data for machine learning QSAR modeling. Ligand-Receptor Contact Fingerprints and scoring values were implemented as descriptor variables. Escalating docking-scoring consensus levels were scanned against orthogonal machine learners, and the best learners (Random Forests and XGBoost) were coupled with genetic algorithm and Shapley additive explanations (SHAP) to identify critical descriptors that determine anti-STAT3 bioactivity to be translated into pharmacophore model(s). Two successful pharmacophores were deduced and subsequently used for in silico screening against the National Cancer Institute (NCI) database. A total of 26 hits were evaluated in vitro for their anti-STAT3 bioactivities. Out of which, three hits of novel chemotypes, showed cytotoxic IC50 values in the nanomolar range (35 nM to 6.7 μM). However, two are potent dihydrofolate reductase (DHFR) inhibitors and therefore should have significant indirect STAT3 inhibitory effects. The third hit (cytotoxic IC50 = 0.44 μM) is purely direct STAT3 inhibitor (devoid of DHFR activity) and caused, at its cytotoxic IC50, more than two-fold reduction in the expression of STAT3 downstream genes (c-Myc and Bcl-xL). The presented work indicates that the concept of data augmentation using multiple docked poses is a promising strategy for generating valid machine learning models capable of discriminating active from inactive compounds.

Hierarchical analysis of the target-based scoring function modification for the example of selected class A GPCRs

Computational methods, especially molecular docking-based calculations, have become indispensable in the modern drug discovery workflow. The constantly increasing chemical space requires fast, robust but most of all highly predictive methods to search for new bioactive agents. Thus, the scoring function (SF) is a useful and broadly applied energy-based element of docking software, allowing quick and effective evaluation of a ligand's propensity to bind to selected protein targets. Despite many spectacular successes of molecular docking applications in virtual screening (VS), the obtained results are often far from ideal, leading to incorrect selection of hit molecules and poor pose prediction. In our study we focused on docking calculation for the selected class A G-protein coupled receptors (GPCRs), with experimentally determined 3D structures and a sufficient set of known ligands with affinity values reported in the ChEMBL database. Our goal is to investigate how much the energy-based scoring function for this particular target class changes when changing from the default to the re-estimated weighting scheme on the specified energy terms in the SF definition. Additionally, we want to verify if indeed more accurate results are obtained when considering different levels of the biological hierarchy, namely: the whole class A GPCRs, sub-subfamilies, or just the individual proteins while applying default or specifically designed weighting coefficients. The performed calculation and evaluation factor values suggest a significant improvement of docking results for the designed SF definition. This individual approach improves the accuracy of binding affinity prediction and active compound recognition. The designed scoring function for classes, sub-subfamilies, or proteins leads to a significant improvement of molecular docking performance, especially at the level of individual proteins. Our results show that to increase the efficiency and predictive power of molecular docking calculations applied in classical VS, the strategy based on the individual approach for scoring function definition for selected proteins should be considered.

Systematically Investigating the Pharmacological Mechanism of Momordica grosvenori in the Treatment of Spinal Cord Injury by Network Pharmacology and Experimental Verification

Objective

This study aimed to explore the molecular mechanism of Momordica grosvenori (MG) in spinal cord injury (SCI) by network pharmacology analysis.

Methods

We searched for potential active MG compounds using the TCMSP database and the BATMAN-TCM platform. The Swiss target prediction database was used to find MG-related targets and the targets of SCI from the CTD, GeneCards, and DrugBank databases. Following that, a protein-protein interaction (PPI) study was carried out. Cytoscape software was used to calculate the hub gene, and R software was used to evaluate the Gene Ontology (GO) and KEGG enrichment pathways. Finally, molecular docking between the hub protein and important compounds was performed. We verified STAT3, MAPK1, HSP90AA1, PIK3R1, PIK3CA, and RXRA potential targets by quantitative PCR.

Results

We obtained 293 MG-anti-SCI targets with potential therapeutic utility by intersecting 346 MG-related targets and 7214 SCI-related targets. The top 10 identified genes, ranking in descending order of value, were SRC, STAT3, MAPK1, HSP90AA1, PIK3R1, PIK3CA, RXRA, AKT1, CREBBP, and JAK2. Through enrichment analysis and literature search, 10 signaling pathways were screened out. The molecular docking of important drugs and hub targets revealed that some had a higher binding affinity. The results of quantitative PCR indicated that MAPK1, RXRA, and STAT3 were expressed differently in in vitro experiments.

Conclusion

In conclusion, the current work indicated that MG might play an anti-SCI role via multicomponent, multitarget, and multichannel interaction, which presents a novel idea for further research into the precise mechanism of MG-anti-SCI interaction.

Structure based Drug Designing Approaches in SARS-CoV-2 Spike Inhibitor Design

The COVID-19 outbreak and the pandemic situation have hastened the research community to design a novel drug and vaccine against its causative organism, the SARS-CoV-2. The spike glycoprotein present on the surface of this pathogenic organism plays an immense role in viral entry and antigenicity. Hence, it is considered an important drug target in COVID-19 drug design. Several three-dimensional crystal structures of this SARS-CoV-2 spike protein have been identified and deposited in the Protein DataBank during the pandemic period. This accelerated the research in computer- aided drug designing, especially in the field of structure-based drug designing. This review summarizes various structure-based drug design approaches applied to this SARS-CoV-2 spike protein and its findings. Specifically, it is focused on different structure-based approaches such as molecular docking, high-throughput virtual screening, molecular dynamics simulation, drug repurposing, and target-based pharmacophore modelling and screening. These structural approaches have been applied to different ligands and datasets such as FDA-approved drugs, small molecular chemical compounds, chemical libraries, chemical databases, structural analogs, and natural compounds, which resulted in the prediction of spike inhibitors, spike-ACE-2 interface inhibitors, and allosteric inhibitors.

A fully differentiable ligand pose optimization framework guided by deep learning and a traditional scoring function

The recently reported machine learning- or deep learning-based scoring functions (SFs) have shown exciting performance in predicting protein-ligand binding affinities with fruitful application prospects. However, the differentiation between highly similar ligand conformations, including the native binding pose (the global energy minimum state), remains challenging that could greatly enhance the docking. In this work, we propose a fully differentiable, end-to-end framework for ligand pose optimization based on a hybrid SF called DeepRMSD+Vina combined with a multi-layer perceptron (DeepRMSD) and the traditional AutoDock Vina SF. The DeepRMSD+Vina, which combines (1) the root mean square deviation (RMSD) of the docking pose with respect to the native pose and (2) the AutoDock Vina score, is fully differentiable; thus is capable of optimizing the ligand binding pose to the energy-lowest conformation. Evaluated by the CASF-2016 docking power dataset, the DeepRMSD+Vina reaches a success rate of 94.4%, which outperforms most reported SFs to date. We evaluated the ligand conformation optimization framework in practical molecular docking scenarios (redocking and cross-docking tasks), revealing the high potentialities of this framework in drug design and discovery. Structural analysis shows that this framework has the ability to identify key physical interactions in protein-ligand binding, such as hydrogen-bonding. Our work provides a paradigm for optimizing ligand conformations based on deep learning algorithms. The DeepRMSD+Vina model and the optimization framework are available at GitHub repository https://github.com/zchwang/DeepRMSD-Vina_Optimization.

Selene-Ethylenelacticamides and N-Aryl-Propanamides as Broad-Spectrum Leishmanicidal Agents

The World Health Organization classifies Leishmania as one of the 17 “neglected diseases” that burden tropical and sub-tropical climate regions with over half a million diagnosed cases each year. Despite this, currently available anti-leishmania drugs have high toxicity and the potential to be made obsolete by parasite drug resistance. We chose to analyze organoselenides for leishmanicidal potential given the reduced toxicity inherent to selenium and the displayed biological activity of organoselenides against Leishmania. Thus, the biological activities of 77 selenoesters and their N-aryl-propanamide derivatives were predicted using robust in silico models of Leishmania infantum, Leishmania amazonensis, Leishmania major, and Leishmania (Viannia) braziliensis. The models identified 28 compounds with >60% probability of demonstrating leishmanicidal activity against L. infantum, and likewise, 26 for L. amazonesis, 25 for L. braziliensis, and 23 for L. major. The in silico prediction of ADMET properties suggests high rates of oral absorption and good bioavailability for these compounds. In the in silico toxicity evaluation, only seven compounds showed signs of toxicity in up to one or two parameters. The methodology was corroborated with the ensuing experimental validation, which evaluated the inhibition of the Promastigote form of the Leishmania species under study. The activity of the molecules was determined by the IC50 value (µM); IC50 values < 20 µM indicated better inhibition profiles. Sixteen compounds were synthesized and tested for their activity. Eight molecules presented IC50 values < 20 µM for at least one of the Leishmania species under study, with compound NC34 presenting the strongest parasite inhibition profile. Furthermore, the methodology used was effective, as many of the compounds with the highest probability of activity were confirmed by the in vitro tests performed.

Is the Triggering of PD-L1 Dimerization a Potential Mechanism for Food-Derived Small Molecules in Cancer Immunotherapy? A Study by Molecular Dynamics

Using small molecules to inhibit the PD-1/PD-L1 pathway is an important approach in cancer immunotherapy. Natural compounds such as capsaicin, zucapsaicin, 6-gingerol and curcumin have been proposed to have anticancer immunologic functions by downregulating the PD-L1 expression. PD-L1 dimerization promoted by small molecules was recently reported to be a potential mechanism to inhibit the PD-1/PD-L1 pathway. To clarify the molecular mechanism of such compounds on PD-L1 dimerization, molecular docking and molecular dynamics simulations were performed. The results evidenced that these compounds could inhibit PD-1/PD-L1 interactions by directly targeting PD-L1 dimerization. Binding free energy calculations showed that capsaicin, zucapsaicin, 6-gingerol and curcumin have strong binding ability with the PD-L1 dimer, where the affinities of them follow the trend of zucapsaicin > capsaicin > 6-gingerol ≈ curcumin. Analysis by residue energy decomposition, contact numbers and nonbonded interactions revealed that these compounds have a tight interaction with the C-sheet, F-sheet and G-sheet fragments of the PD-L1 dimer, which were also involved in the interactions with PD-1. Moreover, non-polar interactions between these compounds and the key residues Ile54, Tyr56, Met115 and Ala121 play a key role in stabilizing the protein−ligand complexes in solution, in which the 4′-hydroxy-3′-methoxyphenyl group and the carbonyl group of zucapsaicin, capsaicin, 6-ginger and curcumin were significant for the complexation of small molecules with the PD-L1 dimer. The conformational variations of these complexes were further analyzed by free energy landscape (FEL) and principal component analysis (PCA) and showed that these small molecules could make the structure of dimers more stable. This work provides a mechanism insight for food-derived small molecules blocking the PD-1/PD-L1 pathway via directly targeting the PD-L1 dimerization and offers theoretical guidance to discover more effective small molecular drugs in cancer immunotherapy.

Current Update on Rotavirus in-Silico Multiepitope Vaccine Design

Rotavirus gastroenteritis is one of the leading causes of pediatric morbidity and mortality worldwide in infants and under-five populations. The World Health Organization (WHO) recommended global incorporation of the rotavirus vaccine in national immunization programs to alleviate the burden of the disease. Implementation of the rotavirus vaccination in certain regions of the world brought about a significant and consistent reduction of rotavirus-associated hospitalizations. However, the efficacy of licensed vaccines remains suboptimal in low-income countries where the incidences of rotavirus gastroenteritis continue to happen unabated. The problem of low efficacy of currently licensed oral rotavirus vaccines in low-income countries necessitates continuous exploration, design, and development of new rotavirus vaccines. Traditional vaccine development is a complex, expensive, labor-intensive, and time-consuming process. Reverse vaccinology essentially utilizes the genome and proteome information on pathogens and has opened new avenues for in-silico multiepitope vaccine design for a plethora of pathogens, promising time reduction in the complete vaccine development pipeline by complementing the traditional vaccinology approach. A substantial number of reviews on licensed rotavirus vaccines and those under evaluation are already available in the literature. However, a collective account of rotavirus in-silico vaccines is lacking in the literature, and such an account may further fuel the interest of researchers to use reverse vaccinology to expedite the vaccine development process. Therefore, the main focus of this review is to summarize the research endeavors undertaken for the design and development of rotavirus vaccines by the reverse vaccinology approach utilizing the tools of immunoinformatics.

In Silico Investigation of the Human GTP Cyclohydrolase 1 Enzyme Reveals the Potential of Drug Repurposing Approaches towards the Discovery of Effective BH4 Therapeutics

The GTP cyclohydrolase 1 enzyme (GTPCH1) is the rate-limiting enzyme of the tetrahydrobiopterin (BH₄) biosynthetic pathway. Physiologically, BH₄ plays a crucial role as an essential cofactor for the production of catecholamine neurotransmitters, including epinephrine, norepinephrine and dopamine, as well as the gaseous signaling molecule, nitric oxide. Pathological levels of the cofactor have been reported in a number of disease states, such as inflammatory conditions, neuropathic pain and cancer. Targeting the GTPCH1 enzyme has great potential in the management of a number of disease pathologies associated with dysregulated BH₄ physiology. This study is an in silico investigation of the human GTPCH1 enzyme using virtual screening and molecular dynamic simulation to identify molecules that can be repurposed to therapeutically target the enzyme. A three-tier molecular docking protocol was employed in the virtual screening of a comprehensive library of over 7000 approved medications and nutraceuticals in order to identify hit compounds capable of binding to the GTPCH1 binding pocket with the highest affinity. Hit compounds were further verified by molecular dynamic simulation studies to provide a detailed insight regarding the stability and nature of the binding interaction. In this study, we identify a number of drugs and natural compounds with recognized anti-inflammatory, analgesic and cytotoxic effects, including the aminosalicylate olsalazine, the antiepileptic phenytoin catechol, and the phlorotannins phlorofucofuroeckol and eckol. Our results suggest that the therapeutic and clinical effects of hit compounds may be partially attributed to the inhibition of the GTPCH1 enzyme. Notably, this study offers an understanding of the off-target effects of a number of compounds and advocates the potential role of aminosalicylates in the regulation of BH₄ production in inflammatory disease states. It highlights an in silico drug repurposing approach to identify a potential means of safely targeting the BH₄ biosynthetic pathway using established therapeutic agents.

Prioritizing Small Sets of Molecules for Synthesis through in-silico Tools: A Comparison of Common Ranking Methods

Prioritizing molecules for synthesis is a key role of computational methods within medicinal chemistry. Multiple tools exist for ranking molecules, from the cheap and popular molecular docking methods to more computationally expensive molecular-dynamics (MD)-based methods. It is often questioned whether the accuracy of the more rigorous methods justifies the higher computational cost and associated calculation time. Here, we compared the performance on ranking the binding of small molecules for seven scoring functions from five docking programs, one end-point method (MM/GBSA), and two MD-based free energy methods (PMX, FEP+). We investigated 16 pharmaceutically relevant targets with a total of 423 known binders. The performance of docking methods for ligand ranking was strongly system dependent. We observed that MD-based methods predominantly outperformed docking algorithms and MM/GBSA calculations. Based on our results, we recommend the application of MD-based free energy methods for prioritization of molecules for synthesis in lead optimization, whenever feasible.

Benchmarked molecular docking integrated molecular dynamics stability analysis for prediction of SARS-CoV-2 papain-like protease inhibition by olive secoiridoids

Objectives

We performed a virtual screening of olive secoiridoids of the OliveNet^TM library to predict SARS-CoV-2 PLpro inhibition. Benchmarked molecular docking protocol that evaluated the performance of two docking programs was applied to execute virtual screening. Molecular dynamics stability analysis of the top-ranked olive secoiridoid docked to PLpro was also carried out.

Methods

Benchmarking virtual screening used two freely available docking programs, AutoDock Vina 1.1.2. and AutoDock 4.2.1. for molecular docking of olive secoiridoids to a single PLpro structure. Screening also included benchmark structures of known active and decoy molecules from the DEKOIS 2.0 library. Based on the predicted binding energies, the docking programs ranked the screened molecules. We applied the usual performance evaluation metrices to evaluate the docking programs using the predicted ranks. Molecular dynamics of the top-ranked olive secoiridoid bound to PLpro and computation of MM-GBSA energy using three iterations during the last 50 ps of the analysis of the dynamics in Desmond supported the stability prediction.

Results and discussions

Predictiveness curves suggested that AutoDock Vina has a better predictive ability than AutoDock, although there was a moderate correlation between the active molecules rankings (Kendall's correlation of rank (τ) = 0.581). Interestingly, two same molecules, Demethyloleuropein aglycone, and Oleuroside enriched the top 1 % ranked olive secoiridoids predicted by both programs. Demethyloleuropein aglycone bound to PLpro obtained by docking in AutoDock Vina when analyzed for stability by molecular dynamics simulation for 50 ns displayed an RMSD, RMSF<2 Å, and MM-GBSA energy of -94.54 ± 6.05 kcal/mol indicating good stability. Molecular dynamics also revealed the interactions of Demethyloleuropein aglycone with binding sites 2 and 3 of PLpro, suggesting a potent inhibition. In addition, for 98 % of the simulation time, two phenolic hydroxy groups of Demethyloleuropein aglycone maintained two hydrogen bonds with Asp302 of PLpro, specifying the significance of the groups in receptor binding.

Conclusion

AutoDock Vina retrieved the active molecules accurately and predicted Demethyloleuropein aglycone as the best inhibitor of PLpro. The Arabian diet consisting of olive products rich in secoiridoids benefits from the PLpro inhibition property and reduces the risk of viral infection.

Specifics of Metabolite-Protein Interactions and Their Computational Analysis and Prediction

Computational approaches to the characterization and prediction of compound-protein interactions have a long research history and are well established, driven primarily by the needs of drug development. While, in principle, many of the computational methods developed in the context of drug development can also be applied directly to the investigation of metabolite-protein interactions, the interactions of metabolites with proteins (enzymes) are characterized by a number of particularities that result from their natural evolutionary origin and their biological and biochemical roles, as well as from a different problem setting when investigating them. In this review, these special aspects will be highlighted and recent research on them and developed computational approaches presented, along with available resources. They concern, among others, binding promiscuity, allostery, the role of posttranslational modifications, molecular steering and crowding effects, and metabolic conversion rate predictions. Recent breakthroughs in the field of protein structure prediction and newly developed machine learning techniques are being discussed as a tremendous opportunity for developing a more detailed molecular understanding of metabolism.

Computational Chemistry and Molecular Modeling of Reversible MAO Inhibitors

Proper elucidation of drug-target interaction is one of the most significant steps at the early stages of the drug development research. Computer-aided drug design tools have substantial contribution to this stage. In this chapter, we specifically concentrate on the computational methods widely used to develop reversible inhibitors for monoamine oxidase (MAO) isozymes. In this context, current computational techniques in identifying the best drug candidates showing high potency are discussed. The protocols of structure-based drug design methodologies, namely, molecular docking, in silico screening, and molecular dynamics simulations, are presented. Employing case studies of safinamide binding to MAO B, we demonstrate how to use AutoDock 4.2.6 and NAMD software packages.

In silico Antivirus Repurposing and its Modification to Organoselenium Compounds as SARS-CoV-2 Spike Inhibitors

&lt;b&gt;Background and Objective:&lt;/b&gt; The COVID-19, which has been circulating since late 2019, is caused by SARS-CoV-2. Because of its high infectivity, this virus has spread widely throughout the world. Spike glycoprotein is one of the proteins found in SARS-CoV-2. Spike glycoproteins directly affect infection by forming ACE-2 receptors on host cells. Inhibiting glycoprotein spikes could be one method of treating COVID-19. In this study, the antivirus marketed as a database will be repurposed into an antiviral SARS-CoV-2 and the selected compounds will be modified to become organoselenium compounds. &lt;b&gt;Materials and Methods:&lt;/b&gt; The research was carried out using &lt;i&gt;in silico&lt;/i&gt; methods, such as rigid docking and flexible docking. To obtain information about the interaction between spike glycoprotein and ligands, MOE 2014.09 was used to perform the molecular docking simulation. &lt;b&gt;Results:&lt;/b&gt; The analysis of binding energy values was used to select the ten best ligands from the first stage of the molecular docking simulation, which was then modified according to the previous QSAR study to produce 96 new molecules. The second stage of molecular docking simulation was performed with modified molecules. The best-modified ligand was chosen by analyzing the ADME-Tox property, RMSD value and binding energy value. &lt;b&gt;Conclusion:&lt;/b&gt; The best three unmodified ligands, Ombitasvir, Elbasvir and Ledipasvir, have a binding energy value of -15.8065, -15.3842 and -15.1255 kcal mol&lt;sup&gt;1&lt;/sup&gt;, respectively and the best three modified ligands ModL1, ModL2 and ModL3 has a binding value of -15.6716, -13.9489 and -13.2951 kcal mol&lt;sup&gt;1&lt;/sup&gt;, respectively with an RMSD value of 1.7109 Å, 2.3179 Å and 1.7836 Å.

Computer-Aided Drug Design: An Update

Computer-aided drug design (CADD) approaches are playing an increasingly important role in understanding the fundamentals of ligand-receptor interactions and helping medicinal chemists design therapeutics. About 5 years ago, we presented a chapter devoted to an overview of CADD methods and covered typical CADD protocols including structure-based drug design (SBDD) and ligand-based drug design (LBDD) approaches that were frequently used in the antibiotic drug design process. Advances in computational hardware and algorithms and emerging CADD methods are enhancing the accuracy and ability of CADD in drug design and development. In this chapter, an update to our previous chapter is provided with a focus on new CADD approaches from our laboratory and other peers that can be employed to facilitate the development of antibiotic therapeutics.

Chemical composition and therapeutic mechanism of Xuanbai Chengqi Decoction in the treatment of COVID-19 by network pharmacology, molecular docking and molecular dynamic analysis

Xuanbai Chengqi Decoction (XBCQD), a classic traditional Chinese medicine, has been widely used to treat COVID-19 in China with remarkable curative effect. However, the chemical composition and potential therapeutic mechanism is still unknown. Here, we used multiple open-source databases and literature mining to select compounds and potential targets for XBCQD. The COVID-19 related targets were collected from GeneCards and NCBI gene databases. After identifying putative targets of XBCQD for the treatment of COVID-19, PPI network was constructed by STRING database. The hub targets were extracted by Cytoscape 3.7.2 and MCODE analysis was carried out to extract modules in the PPI network. R 3.6.3 was used for GO enrichment and KEGG pathway analysis. The effective compounds were obtained via network pharmacology and bioinformatics analysis. Drug-likeness analysis and ADMET assessments were performed to select core compounds. Moreover, interactions between core compounds and hub targets were investigated through molecular docking, molecular dynamic (MD) simulations and MM-PBSA calculations. As a result, we collected 638 targets from 61 compounds of XBCQD and 845 COVID-19 related targets, of which 79 were putative targets. Based on the bioinformatics analysis, 10 core compounds and 34 hub targets of XBCQD for the treatment of COVID-19 were successfully screened. The enrichment analysis of GO and KEGG indicated that XBCQD mainly exerted therapeutic effects on COVID-19 by regulating signal pathways related to viral infection and inflammatory response. Meanwhile, the results of molecular docking showed that there was a stable binding between the core compounds and hub targets. Moreover, MD simulations and MM-PBSA analyses revealed that these compounds exhibited stable conformations and interacted well with hub targets during the simulations. In conclusion, our research comprehensively explained the multi-component, multi-target, and multi-pathway intervention mechanism of XBCQD in the treatment of COVID-19, which provided evidence and new insights for further research.

Potential targets of severe acute respiratory syndrome coronavirus 2 of clinical drug fluvoxamine: Docking and molecular dynamics studies to elucidate viral action

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continued evolving for survival and adaptation by mutating itself into different variants of concern, including omicron. Several studies and clinical trials found fluvoxamine, an Food and Drug Administration-approved antidepressant drug, to be effective at preventing mild coronavirus disease 2019 (COVID-19) from progressing to severe diseases. However, the mechanism of fluvoxamine's direct antiviral action against COVID-19 is still unknown. Fluvoxamine was docked with 11 SARS-CoV-2 targets and subjected to stability, conformational changes, and binding free energy analyses to explore its mode of action. Of the targets, nonstructural protein 14 (NSP14), main protease (Mpro), and papain-like protease (PLpro) had the best docking scores with fluvoxamine. Consistent with the docking results, it was confirmed by molecular dynamics simulations that the NSP14 N7-MTase ((N7-guanine)-methyltransferase)-fluvoxamine, Mpro-fluvoxamine, and PLpro-fluvoxamine complexes are stable, with the lowest binding free energies of -105.1, -82.7, and - 38.5 kJ/mol, respectively. A number of hotspot residues involved in the interaction were also identified. These include Glu166, Asp187, His41, and Cys145 in Mpro, Gly163 and Arg166 in PLpro, and Glu302, Gly333, and Phe426 in NSP14, which could aid in the development of better antivirals against SARS-CoV-2.

Potential pharmacological mechanisms of four active compounds of Macleaya cordata extract against enteritis based on network pharmacology and molecular docking technology

Background: Macleaya cordata extract (MCE) is effective in the treatment of enteritis, but its mechanism has not been fully elucidated. Therefore, this study combined network pharmacology and molecular docking technologies to investigate the potential pharmacological mechanism of MCE in the treatment of enteritis. Methods: The information of active compounds in MCE was accessed through the literature. Furthermore, PubChem, PharmMapper, UniProt, and GeneCards databases were used to analyze the targets of MCE and enteritis. The intersection of drug and disease targets was imported into the STRING database, and the analysis results were imported into Cytoscape 3.7.1 software to construct a protein-protein interaction (PPI) network and to screen core targets. The Metascape database was used for conducting Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. AutoDock Tools software was used for the molecular docking of active compounds with the core targets. Results: MCE has four active compounds, namely, sanguinarine, chelerythrine, protopine, and allocryptopine, and a total of 269 targets after de-duplication. Furthermore, a total of 1,237 targets were associated with enteritis, 70 of which were obtained by aiding the drug-disease intersection with the aforementioned four active compound targets of MCE. Five core targets including mitogen-activated protein kinase 1 (MAPK1) and AKT serine/threonine kinase 1 (AKT1) were obtained using the PPI network, which are considered the potential targets for the four active compounds of MCE in the treatment of enteritis. The GO enrichment analysis involved 749 biological processes, 47 cellular components, and 64 molecular functions. The KEGG pathway enrichment analysis revealed 142 pathways involved in the treatment of enteritis by the four active compounds of MCE, among which PI3K-Akt and MAPK signaling pathways were the most important pathways. The results of molecular docking showed that the four active compounds demonstrated good binding properties at the five core targets. Conclusion: The pharmacological effects of the four active compounds of MCE in the treatment of enteritis involve acting on signaling pathways such as PI3K-Akt and MAPK through key targets such as AKT1 and MAPK1, thus providing new indications for further research to verify its mechanisms.