In silico study of medicinal plants with cyclodextrin inclusion complex as the potential inhibitors against SARS-CoV-2 main protease (Mpro) and spike (S) receptor

In Silico Docking of Medicinal Herbs Against P. gingivalis for Chronic Periodontitis Intervention

OBJECTIVE

This study aimed to explore the therapeutic potential of medicinal herbs for chronic periodontitis by examining the molecular interactions between specific herbal compounds and the heme-binding protein of Porphyromonas gingivalis, a key pathogen involved in the disease.

METHODS

The crystal structure of heme-binding protein was obtained from the Protein Data Bank. Herbal compounds were identified through an extensive literature review. Molecular docking simulations were performed to predict binding affinities, followed by Absorption, Distribution, Metabolism, and Excretion (ADME) parameter prediction. Drug-likeness was assessed based on Lipinski's Rule of Five, and pharmacophore modeling was conducted to identify key molecular interactions.

RESULTS

The molecular docking simulations revealed that chelidonine, rotenone, and myricetin exhibited significant binding affinities to the heme-binding protein, with docking scores of -6.5 kcal/mol, -6.4 kcal/mol, and -6.1 kcal/mol, respectively. These compounds formed stable interactions with key amino acid residues within the binding pocket. ADME analysis indicated that all 3 compounds had favourable pharmacokinetic properties, with no violations of Lipinski's rules and minimal predicted toxicity. Pharmacophore modeling further elucidated the interaction profiles, highlighting specific hydrogen bonds and hydrophobic interactions critical for binding efficacy.

CONCLUSIONS

Chelidonine, rotenone, and myricetin emerged as promising therapeutic candidates for chronic periodontitis due to their strong binding affinities, favorable ADME profiles, and lack of significant toxicity. The detailed pharmacophore modeling provided insights into the molecular mechanisms underpinning their inhibitory effects on the heme-binding protein of P. gingivalis. These findings suggest that these compounds have the potential for further development as effective treatments for chronic periodontitis. Future research should focus on in vitro and in vivo validation of these findings to confirm the efficacy and safety of these compounds in biological systems.

Unveiling cyclodextrin conjugation as multidentate excipients: An exploratory journey across industries

The discovery of branched molecules like dextrin by Schardinger in 1903 marked the inception of cyclodextrin (CD) utilization, catalyzing its journey from laboratory experimentation to widespread commercialization within the pharmaceutical industry. CD, a cyclic oligosaccharide containing glucopyranose units, acts as a versatile guest molecule, forming inclusion complexes (ICs) with various host molecules. Computational studies have become instrumental in elucidating the intricate interactions between β-CD and guest molecules, enabling the prediction of binding energy, forces, affinity, and complex stability. The computational approach has established robust correlations with experimental outcomes, enhancing our understanding of CD-mediated complexation phenomena. This comprehensive review delves into the CD based Inclusion complex (CDIC) formation and a myriad of components, including drug molecules, amino acids, vitamins, and volatile oils. These complexes find applications across diverse industries, ranging from pharmaceuticals to nutraceuticals, food, fragrance, and beyond. In the pharmaceutical realm, β- CDICs offer innovative solutions for enhancing drug solubility, stability, and bioavailability, thus overcoming formulation challenges associated with poorly water-soluble drugs. Furthermore, the versatility of CDs extends beyond pharmaceuticals, with applications in the encapsulation of phytoactive compounds in nutraceuticals and the enhancing flavor, aroma in food and fragrance industries. This review underscores the pivotal role of CDs conjugation in modern drug delivery systems, emphasizing the importance of interdisciplinary approaches that integrate computational modeling with experimental validation. As the pharmaceutical landscape continues to evolve, CDs-based formulations stand poised to drive innovation and address the ever-growing demand for efficacious and patient-friendly drug delivery solutions.

Unlocking the wound-healing potential: An integrative in silico proteomics and in vivo analysis of Tacorin, a bioactive protein fraction from Ananas comosus (L.) Merr. Stem

Tacorin, a bioactive protein fraction derived from pineapple stem (Ananas comosus), has emerged as a promising therapeutic agent for wound healing. This study employs an integrated approach, combining in silico proteomics and in vivo investigations, to unravel the molecular mechanisms underlying Tacorin's wound healing properties. In the domain of in silico proteomics, the composition of Tacorin is elucidated through LC/MS-MS protein sequencing, revealing ananain (23.77 kDa) and Jacalin-like lectin (14.99 kDa) as its predominant constituents. Molecular protein-protein docking simulations unveil favorable interactions between Tacorin's components and key regulators of wound healing, including TGF-β, TNF-α, and MMP-2. The calculated free binding energies indicate strong binding affinities between Tacorin proteins and their target receptors. Specifically, ananain demonstrates a binding affinity of -12.2 kcal/mol with TGF-β, suggesting its potential as a potent activator of TGF-β-mediated signaling, while Jacalin-like lectin exhibits the most favorable binding affinity of -8.7 kcal/mol with TNF-α. Subsequent 100 ns molecular dynamics (MD) simulations provide insights into the dynamic behavior and stability of Tacorin-receptor complexes, shedding light on the molecular determinants of Tacorin's therapeutic effects. Complementing the in silico analyses, in vivo studies evaluate Tacorin's efficacy in wound healing using skin and uterine incision models. Tacorin treatment accelerates wound closure and promotes tissue repair in both models, as evidenced by macroscopic observations and histological assessments. Overall, this study provides compelling evidence of Tacorin's therapeutic potential in wound healing and underscores the importance of elucidating its molecular mechanisms for further development and clinical translation.

In silico exploration of bioactive secondary metabolites with anesthetic effects on sodium channels Nav 1.7, 1.8, and 1.9 in painful human dental pulp

AIM

To investigate the efficacy of medicinal plant bioactive secondary metabolites as inhibitors of voltage-gated sodium channels (Nav1.7, Nav1.8, and Nav1.9) in managing painful states of dental pulps.

METHODOLOGY

Molecular docking, ADME prediction, toxicity profiling, and pharmacophore modeling were used to assess the binding affinities, pharmacokinetic properties, toxicological profiles, and active pharmacophores of the selected bioactive compounds.

RESULTS

Three compounds (Sepaconitine, Lappaconitine, and Ranaconitine) showed binding affinities (ΔG = -8.95 kcal/mol, -7.77 kcal/mol, and -7.44 kcal/mol, respectively) with all three Nav1.7, Nav1.8, and Nav1.9 sodium channels. The sepaconitine amine group formed hydrophobic interactions with key residues. The Lappaconitine benzene ring contributed to hydrophobic interactions and hydrogen bond acceptor interactions. The hydrophobic interactions of the ranaconitine amine group play a critical role with specific residues on Nav1.8 and Nav1.9.

CONCLUSION

The natural fusicoccane diterpenoid derivatives Sepaconitine, Lappaconitine, and Ranaconitine are potential lead compounds for the development of novel analgesics as selective antihyperalgesic drugs, which will provide a new dental pharmacological intervention for managing painful dental pulp conditions. Further experimental validation and clinical studies that confirm the efficacy and safety of these compounds will strengthen their applicability in dental practice.

Development of an Antiviral Medicinal Plant and Natural Product Database (avMpNp Database) from Biodiversity

The construction of compound databases (DB) is a strategy for the rational search of bioactive compounds and drugs for new and old diseases. In order to bring greater impact to drug discovery, we propose the development of a DB of bioactive antiviral compounds. Several research groups have presented evidence of the antiviral activity of medicinal plants and compounds isolated from these plants. We believe that compiling these discoveries in a DB would benefit the scientific research community and increase the speed to discover new potential drugs and medicines. Thus, we present the Antiviral Medicinal Plant and Natural Product DB (avMpNp DB) as an important source for acquiring, organizing, and distributing knowledge related to natural products and antiviral drug discovery. The avMpNp DB contains a series of chemically diverse compounds with drug-like profiles. To test the potential of this DB, SARS-CoV-2 Mpro and PLpro enzymatic inhibition assays were performed for available compounds resulting in IC50 values ranging from 6.308±0.296 to 15.795±0.155 μM. As a perspective, artificial intelligence tools will be added to implement computational predictions, as well as other chemical functionalities that allow data validation.

Unraveling modulation effects on albumin synthesis and inflammation by Striatin, a bioactive protein fraction isolated from Channa striata: In silico proteomics and in vitro approaches

Hypoalbuminemia, associated with inflammation in severely ill patients, can emerge due to decreased albumin production. Transforming growth factor-beta (TGF-β) and nuclear factor-kappa B (NF-κB) are critical signaling pathways responsible for decreased albumin expression. This study explores the protein content and modulation effects of Striatin on albumin synthesis and inflammation, employing in silico proteomics and in vitro investigations. In the in silico proteomics realm, LC/MS-MS protein sequencing, 3D modeling, protein-protein docking simulations, 100 ns molecular dynamics (MD) simulations, and MM/PBSA binding free energy calculations were carried out. Complementing this, in vitro studies examined Albumin gene expression and extracellular secretion in HepG2 cells subjected to lipopolysaccharides-induced hypoalbuminemia. Furthermore, the study probed Striatin's influence on the NF-ᴋB expression, given albumin's role as a negative acute-phase protein. The results unveiled nucleoside diphosphate kinase (NdK) and parvalbumin (PV) as the prominent constituents within Striatin. Notably, NdK and PV exhibited the ability to disrupt hydrogen bonds with specific residues in both TGF-β and NF-κB complexes, thereby enhancing their flexibility, akin to the action of the FKBP12 complex (antagonist complex). In the in vitro experiments, Striatin demonstrated a dose and time-dependent inhibition of hypoalbuminemia, with peak efficacy observed at a concentration of 20 μg/mL. At this concentration, Striatin also suppressed NF-κB expression when co-incubated with lipopolysaccharides. While these findings suggest potential inhibitory effects of Striatin on TGF-β and NF-κB activities, they are preliminary and warrant further investigation. This study highlights Striatin's potential as a therapeutic agent for inflammation-related hypoalbuminemia, though additional research is needed to fully validate these results.

Leveraging Therapeutic Proteins and Peptides from Lumbricus Earthworms: Targeting SOCS2 E3 Ligase for Cardiovascular Therapy through Molecular Dynamics Simulations

Suppressor of cytokine signaling 2 (SOCS2), an E3 ubiquitin ligase, regulates the JAK/STAT signaling pathway, essential for cytokine signaling and immune responses. Its dysregulation contributes to cardiovascular diseases (CVDs) by promoting abnormal cell growth, inflammation, and resistance to cell death. This study aimed to elucidate the molecular mechanisms underlying the interactions between Lumbricus-derived proteins and peptides and SOCS2, with a focus on identifying potential therapeutic candidates for CVDs. Utilizing a multifaceted approach, advanced computational methodologies, including 3D structure modeling, protein-protein docking, 100 ns molecular dynamics (MD) simulations, and MM/PBSA calculations, were employed to assess the binding affinities and functional implications of Lumbricus-derived proteins on SOCS2 activity. The findings revealed that certain proteins, such as Lumbricin, Chemoattractive glycoprotein ES20, and Lumbrokinase-7T1, exhibited similar activities to standard antagonists in modulating SOCS2 activity. Furthermore, MM/PBSA calculations were employed to assess the binding free energies of these proteins with SOCS2. Specifically, Lumbricin exhibited an average ΔGbinding of -59.25 kcal/mol, Chemoattractive glycoprotein ES20 showed -55.02 kcal/mol, and Lumbrokinase-7T1 displayed -69.28 kcal/mol. These values suggest strong binding affinities between these proteins and SOCS2, reinforcing their potential therapeutic efficacy in cardiovascular diseases. Further in vitro and animal studies are recommended to validate these findings and explore broader applications of Lumbricus-derived proteins.

In silico assessment of biocompatibility and toxicity: molecular docking and dynamics simulation of PMMA-based dental materials for interim prosthetic restorations

AIM

This study aimed to comprehensively assess the biocompatibility and toxicity profiles of poly(methyl methacrylate) (PMMA) and its monomeric unit, methyl methacrylate (MMA), crucial components in dental materials for interim prosthetic restorations.

METHODOLOGY

Molecular docking was employed to predict the binding affinities, energetics, and steric features of MMA and PMMA with selected receptors involved in bone metabolism and tissue development, including RANKL, Fibronectin, BMP9, NOTCH2, and other related receptors. The HADDOCK standalone version was utilized for docking calculations, employing a Lamarckian genetic algorithm to explore the conformational space of ligand-receptor interactions. Furthermore, molecular dynamics (MD) simulations over 100 nanoseconds were conducted using the GROMACS package to evaluate dynamic actions and structural stability. The LigandScout was utilized for pharmacophore modeling, which employs a shape-based screening approach to identify potential ligand binding sites on protein targets.

RESULTS

The molecular docking studies elucidated promising interactions between PMMA and MMA with key biomolecular targets relevant to dental applications. MD simulation results provided strong evidence supporting the structural stability of PMMA complexes over time. Pharmacophore modeling highlighted the significance of carbonyl and hydroxyl groups as pharmacophoric features, indicating compounds with favorable biocompatibility profiles.

CONCLUSION

This study underscores the potential of PMMA in dental applications, emphasizing its structural stability, molecular interactions, and safety considerations. These findings lay a foundation for future advancements in dental biomaterials, guiding the design and optimization of materials for enhanced biocompatibility. Future directions include experimental validation of computational findings and the development of PMMA-based dental materials with improved biocompatibility and clinical performance.

Unraveling potential neuroprotective mechanisms of herbal medicine for Alzheimer's diseases through comprehensive molecular docking analyses

Alzheimer's disease (AD) continues to be a worldwide health concern, demanding innovative therapeutic approaches. This study investigates the neuroprotective potential of herbal compounds by scrutinizing their interactions with Beta-Secretase-1 (BACE1). Through comprehensive molecular docking analyses, three compounds, Masticadienonic acid (ΔG: -9.6 kcal/mol), Hederagenin (ΔG: -9.3 kcal/mol), and Anthocyanins (ΔG: -8.1 kcal/mol), emerge as promising BACE1 ligands, displaying low binding energies and strong affinities. ADME parameter predictions, drug-likeness assessments, and toxicity analyses reveal favorable pharmacokinetic profiles for these compounds. Notably, Masticadienonic Acid exhibits optimal drug-likeness (-3.3736) and negligible toxicity concerns. Hederagenin (drug-likeness: -5.3272) and Anthocyanins (drug-likeness: -6.2041) also demonstrate promising safety profiles. Furthermore, pharmacophore modeling elucidates the compounds' unique interaction landscapes within BACE1's active site. Masticadienonic acid showcases seven hydrophobic interactions and a hydrogen bond acceptor interaction with Thr232. Hederagenin exhibits a specific hydrogen bond acceptor interaction with Trp76, emphasizing its selective binding. Anthocyanins reveal a multifaceted engagement, combining hydrophobic contacts and hydrogen bond interactions with key residues. In conclusion, Masticadienonic acid, Hederagenin, and Anthocyanins stand out as promising candidates for further experimental validation, presenting a synergistic balance of efficacy and safety in combating AD through BACE1 inhibition.

Dental biomaterials redefined: molecular docking and dynamics-driven dental resin composite optimization

BACKGROUND

Dental resin-based composites are widely recognized for their aesthetic appeal and adhesive properties, which make them integral to modern restorative dentistry. Despite their advantages, adhesion and biomechanical performance challenges persist, necessitating innovative strategies for improvement. This study addressed the challenges associated with adhesion and biomechanical properties in dental resin-based composites by employing molecular docking and dynamics simulation.

METHODS

Molecular docking assesses the binding energies and provides valuable insights into the interactions between monomers, fillers, and coupling agents. This investigation prioritizes SiO2 and TRIS, considering their consistent influence. Molecular dynamics simulations, executed with the Forcite module and COMPASS II force field, extend the analysis to the mechanical properties of dental composite complexes. The simulations encompassed energy minimization, controlled NVT and NPT ensemble simulations, and equilibration stages. Notably, the molecular dynamics simulations spanned a duration of 50 ns.

RESULTS

SiO2 and TRIS consistently emerged as influential components, showcasing their versatility in promoting solid interactions. A correlation matrix underscores the significant roles of van der Waals and desolvation energies in determining the overall binding energy. Molecular dynamics simulations provide in-depth insights into the mechanical properties of dental composite complexes. HEMA-SiO2-TRIS excelled in stiffness, BisGMA-SiO2-TRIS prevailed in terms of flexural strength, and EBPADMA-SiO2-TRIS offered a balanced combination of mechanical properties.

CONCLUSION

These findings provide valuable insights into optimizing dental composites tailored to diverse clinical requirements. While EBPADMA-SiO2-TRIS demonstrates distinct strengths, this study emphasizes the need for further research. Future investigations should validate the computational findings experimentally and assess the material's response to dynamic environmental factors.

Revisiting methotrexate and phototrexate Zinc15 library-based derivatives using deep learning in-silico drug design approach

Introduction: Cancer is the second most prevalent cause of mortality in the world, despite the availability of several medications for cancer treatment. Therefore, the cancer research community emphasized on computational techniques to speed up the discovery of novel anticancer drugs. Methods: In the current study, QSAR-based virtual screening was performed on the Zinc15 compound library (271 derivatives of methotrexate (MTX) and phototrexate (PTX)) to predict their inhibitory activity against dihydrofolate reductase (DHFR), a potential anticancer drug target. The deep learning-based ADMET parameters were employed to generate a 2D QSAR model using the multiple linear regression (MPL) methods with Leave-one-out cross-validated (LOO-CV) Q2 and correlation coefficient R2 values as high as 0.77 and 0.81, respectively. Results: From the QSAR model and virtual screening analysis, the top hits (09, 27, 41, 68, 74, 85, 99, 180) exhibited pIC50 ranging from 5.85 to 7.20 with a minimum binding score of -11.6 to -11.0 kcal/mol and were subjected to further investigation. The ADMET attributes using the message-passing neural network (MPNN) model demonstrated the potential of selected hits as an oral medication based on lipophilic profile Log P (0.19-2.69) and bioavailability (76.30% to 78.46%). The clinical toxicity score was 31.24% to 35.30%, with the least toxicity score (8.30%) observed with compound 180. The DFT calculations were carried out to determine the stability, physicochemical parameters and chemical reactivity of selected compounds. The docking results were further validated by 100 ns molecular dynamic simulation analysis. Conclusion: The promising lead compounds found endorsed compared to standard reference drugs MTX and PTX that are best for anticancer activity and can lead to novel therapies after experimental validations. Furthermore, it is suggested to unveil the inhibitory potential of identified hits via in-vitro and in-vivo approaches.

Using TransR to enhance drug repurposing knowledge graph for COVID-19 and its complications

MOTIVATION

The COVID-19 pandemic has been spreading globally for four years, yet specific drugs that effectively suppress the virus remain elusive. Furthermore, the emergence of complications associated with COVID-19 presents significant challenges, making the development of therapeutics for COVID-19 and its complications an urgent task. However, traditional drug development processes are time-consuming. Drug repurposing, which involves identifying new therapeutic applications for existing drugs, presents a viable alternative.

RESULT

In this study, we construct a knowledge graph by retrieving information on genes, drugs, and diseases from databases such as DRUGBANK and GNBR. Next, we employ the TransR knowledge representation learning approach to embed entities and relationships into the knowledge graph. Subsequently, we train the knowledge graph using a graph neural network model based on TransR scoring. This trained knowledge graph is then utilized to predict drugs for the treatment of COVID-19 and its complications. Based on experimental results, we have identified 15 drugs out of the top 30 with the highest success rates associated with treating COVID-19 and its complications. Notably, out of these 15 drugs, 10 specifically aimed at treating COVID-19, such as Torcetrapib and Tocopherol, has not been previously identified in the knowledge graph. This finding highlights the potential of our model in aiding healthcare professionals in drug development and research related to this disease.

Omics data analysis reveals common molecular basis of small cell lung cancer and COVID-19

The impact of COVID-19 infection on individuals with small cell lung cancer (SCLC) poses a serious threat. Unfortunately, the molecular basis of this severe comorbidity has yet to be elucidated. The present study addresses this gap utilizing publicly available omics data of COVID-19 and SCLC to explore the key molecules and associated pathways involved in the convergence of these diseases. Findings revealed 402 genes, that exhibited differential expression patterns in SCLC patients and also play a pivotal role in COVID-19 pathogenesis. Subsequent functional enrichment analyses identified relevant ontologies and pathways that are significantly associated with these genes, revealing important insights into their potential biological, molecular and cellular functions. The protein-protein interaction network, constructed under four combinatorial topological assessments, highlighted SMAD3, CAV1, PIK3R1, and FN1 as the primary components to this comorbidity. Our results suggest that these components significantly regulate this cross-talk triggering the PI3K-AKT and TGF-β signaling pathways. Lastly, this study made a multi-step computational attempt and identified corylifol A and ginkgetin from natural sources that can potentially inhibit these components. Therefore, the outcomes of this study offer novel perspectives on the common molecular mechanisms underlying SCLC and COVID-19 and present future opportunities for drug development.Communicated by Ramaswamy H. Sarma.

Tenofovir antiviral drug solubility enhancement with β-cyclodextrin inclusion complex and in silico study of potential inhibitor against SARS-CoV-2 main protease (Mpro)

Tenofovir (TFR) is an antiviral drug commonly used to fight against viral diseases infection due to its good potency and high genetic barrier to drug resistance. In physiological conditions, TFR is less water soluble, more unstable, and less permeable, limiting its effective therapeutic applications. In addition to their use in treating the Coronavirus disease 2019 (COVID-19), cyclodextrins (CDs) are also being used as a molecule to develop therapies for other diseases due to its enhance solubility and stability. This study is designed to synthesize and characterization of β-CD:TFR inclusion complex and its interaction against SARS-CoV-2 (M^Pro) protein (PDB ID;7cam). Several techniques were used to characterize the prepared β-CD:TFR inclusion complex, including UV-Visible, FT-IR, XRD, SEM, TGA, and DSC, which provided appropriate evidence to confirm the formation. A 1:1 stoichiometry was determined for β-CD:TFR inclusion complex in aqueous medium from UV-Visible absorption spectra by using the Benesi-Hildebrand method. Phase solubility studies proposed that β-CD enhanced the excellent solubility of TFR and the stability constant was obtained at 863 ± 32 M^-1. Moreover, the molecular docking confirmed the experimental results demonstrated the most desirable mode of TFR encapsulated into the β-CD nanocavity via hydrophobic interactions and possible hydrogen bonds. Moreover, TFR was validated in the β-CD:TFR inclusion complex as potential inhibitors against SARS-CoV-2 main protease (M^pro) receptors by using in silico methods. The enhanced solubility, stability, and antiviral activity against SARS-CoV-2 (M^Pro) suggest that β-CD:TFR inclusion complexes can be further used as feasible water-insoluble antiviral drug carriers in viral disease infection.

SARS-CoV-2 main protease (3CLpro) interaction with acyclovir antiviral drug/methyl-β-cyclodextrin complex: Physiochemical characterization and molecular docking

During the current outbreak of the novel coronavirus disease 2019 (COVID-19), researchers have examined several antiviral drugs with the potential to inhibit the proliferation of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The antiviral drug acyclovir (AVR), which is used to treat COVID-19, in complex with methyl-β-cyclodextrin (Mβ-CD) was examined in the solution and solid phases. UV-visible and fluorescence spectroscopic analyses confirmed that the guest (AVR) was included inside the host (Mβ-CD) cavity. A solid inclusion complex of AVR was prepared by co-precipitation, physical mixing, kneading, and bath sonication methods at a 1:1 ratio of Mβ-CD:AVR. The prepared Mβ-CD:AVR inclusion complex was characterized using Fourier transform infrared spectroscopy (FTIR), X-ray powder diffraction (XRD), thermogravimetric analysis (TGA), differential scanning calorimetry (DSC), and scanning electron microscopy (SEM) analysis. Phase solubility studies indicated the Mβ-CD:AVR inclusion complex exhibited a higher stability constant and linear enhancement in AVR solubility with increasing Mβ-CD concentrations. In silico analysis of the Mβ-CD/AVR inclusion complex confirmed that AVR drugs show potential as inhibitors of SARS-CoV-2 3C-like protease (3CL^pro) receptors. Results obtained using the PatchDock and FireDock servers indicated that the most favorable docking ligand was Mβ-CD:AVR, which interacted with SARS-CoV-2 (3CL^Pro) protease inhibitors with high geometric shape complementarity scores (2522 and 5872) and atomic contact energy (-313.77 and -214.70 kcal mol^-1). Our results suggest that the Mβ-CD/AVR inclusion complex inhibits the main protease of SARS-CoV-2, although further wet-lab experiments are needed to verify these findings.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

Heterologous expression and physicochemical characteristics identification of Kunitz protease inhibitor in Brassica napus

A Kunitz protease inhibitor gene (RTI; rti) was cloned from rapeseed and expressed in a Pichia pastoris expression system for the first time. After isolation and purification, the physical and chemical characteristics of the inhibitor were analyzed. The results showed that the induced expression level of the recombinant RTI reached 628 mg/L, and the specific activity of the inhibitor reached 69.6 TIU/mg protein at the shake flask fermentation level; the recombinant RTI retained more than 70% inhibitory activity between 30 and 90 °C and more than 80% inhibitory activity between pH 2.0-11.0. The metal ions Cu²⁺ and CO²⁺ and the organic reagents methanol, ethanol, acetone, and chloroform inhibit its activity. The recombinant RTI interacts with trypsin in a noncompetitive manner and has a strong and specific inhibitory effect on trypsin, a typical Kunitz trypsin inhibitor from plants. Combined with its good physical and chemical properties, recombinant RTI has the potential to be developed into an insect resistance protein.