Interaction between β-lactoglobulin and EGCG under high-pressure by molecular dynamics simulation

The binding between β-lactoglobulin and epigallocatechin gallate (EGCG) under the pressure of 600 MPa was explored using molecular docking and molecular dynamics (MD) simulation. EGCG bound mainly in two regions with site 1 in internal cavity of the β-barrel and site 2 on the surface of protein. 150 ns MD was performed starting from the structure with the optimal binding energy at the two sites in molecular docking, respectively. It was found that the protein fluctuated greatly when small molecule bound to site 2 at 0.1 MPa, and the protein fluctuation and solvent accessible surface area became smaller under high-pressure. The binding of small molecules made the protein structure more stable with increasing of α-helix and β-sheet, while high-pressure destroyed α-helix of protein. The binding energy of small molecules at site 1was stronger than that at site 2 under 0.1 MPa, with stronger van der Waals and hydrophobic interaction at site 1 while more hydrogen bonds were present at site 2. The binding energy of both sites weakened under high-pressure, especially at site 1, causing the binding force to be weaker at site 1 than that at site 2 under high-pressure.

Mutations in RBD of SARS-CoV-2 Omicron variant result stronger binding to human ACE2 protein

The spreading of SARS-CoV-2 virus resulted the COVID-19 pandemic, which has caused more than 5 millions of death globally. Several major variants of SARS-CoV-2 have emerged and placed challenges in controlling the infections. The recently emerged Omicron variant raised serious concerns about reducing efficacy of antibodies or vaccines, due to its vast mutations. We modelled the complex structure of human ACE2 protein and the receptor binding domain of Omicron variant, then conducted atomistic molecular dynamics simulations to study the binding interactions. The analysis shows that the Omicron variant RBD binds more strongly to the human ACE2 protein than the original strain. The mutation at the ACE2-RBD interface enhanced the tight binding by increasing hydrogen bonding interaction and enlarging buried solvent accessible surface area.

A Binary Matrix Method to Enumerate, Hierarchically Order and Structurally Classify Peptide Aggregation

Protein aggregation is a common and complex phenomenon in biological processes, yet a robust analysis of this aggregation process remains elusive. The commonly used methods such as centre-of-mass to centre-of-mass (COM-COM) distance, the radius of gyration (Rg), hydrogen bonding (HB) and solvent accessible surface area (SASA) do not quantify the aggregation accurately. Herein, a new and robust method that uses an aggregation matrix (AM) approach to investigate peptide aggregation in a MD simulation trajectory is presented. A nxn two-dimensional aggregation matrix (AM) is created by using the inter-peptide CA-CA cut-off distances which are binarily encoded (0 or 1). These aggregation matrices are analysed to enumerate, hierarchically order and structurally classify the aggregates. Moreover, the comparison between the present AM method and the conventional Rg, COM-COM and HB methods shows that the conventional methods grossly underestimate the aggregation propensity. Additionally, the conventional methods do not address the hierarchy and structural ordering of the aggregates, which the present AM method does. Finally, the present AM method utilises only nxn two-dimensional matrices to analyse aggregates consisting of several peptide units. To the best of our knowledge, this is a maiden approach to enumerate, hierarchically order and structurally classify peptide aggregation.

Identifying signatures of proteolytic stability and monomeric propensity in O-glycosylated insulin using molecular simulation

Insulin has been commonly adopted as a peptide drug to treat diabetes given its ability to facilitate the uptake of glucose from the blood. The development of oral insulin remains elusive over decades owing to its susceptibility to the enzymes in the gastrointestinal tract and poor permeability through the intestinal epithelium upon dimerization. Recent experimental studies have revealed that certain O-linked glycosylation patterns could enhance insulin’s proteolytic stability and reduce its dimerization propensity, but the understanding of such phenomena at the molecular level is still evasive. To address this challenge, we propose and test several structural determinants that could potentially in uence insulin’s proteolytic stability and dimerization propensity. We used these as the metrics to assess the properties of interest from 10 s aggregate molecular dynamics of each of 12 targeted insulin glyco-variants from multiple wild-type crystal structures. We found that glycan-involved hydrogen bonds and glycan-dimer occlusion were useful metrics predicting the proteolytic stability and dimerization propensity of insulin, as was in part the solvent accessible surface area of proteolytic sites, while other plausible metrics were not generally predictive. This work helps better explain how O-linked glycosylation in uences the proteolytic stability and monomeric propensity of insulin, illuminating a path towards rational molecular design of insulin glycoforms.

Self-Assembly of NaOL-DDA Mixtures in Aqueous Solution: A Molecular Dynamics Simulation Study

The self-assembly behaviors of sodium oleate (NaOL), dodecylamine (DDA), and their mixtures in aqueous solution were systematically investigated by large-scale molecular dynamics simulations, respectively. The interaction mechanisms between the surfactants, as well as the surfactants and solvent, were revealed via the radial distribution function (RDF), cluster size, solvent-accessible surface area (SASA), hydrogen bond, and non-bond interaction energy. Results showed that the molecules more easily formed aggregates in mixed systems compared to pure systems, indicating higher surface activity. The SASA values of DDA and NaOL decreased significantly after mixing, indicating a tighter aggregation of the mixed surfactants. The RDF results indicated that DDA and NaOL strongly interacted with each other, especially in the mixed system with a 1:1 molar ratio. Compared to van der Waals interactions, electrostatic interactions between the surfactant molecules were the main contributors to the improved aggregation in the mixed systems. Besides, hydrogen bonds were found between NaOL and DDA in the mixed systems. Therefore, the aggregates in the mixed systems were much more compact in comparison with pure systems, which contributed to the reduction of the repulsive force between same molecules. These findings indicated that the mixed NaOL/DDA surfactants had a great potential in application of mineral flotation.

Identification of Benzothiazole‒Rhodanine Derivatives as α-Amylase and α-Glucosidase Inhibitors: Design, Synthesis, In-silico and In-Vitro Analysis

In search for possible antidiabetic agents, a new series of Benzothiazole-Rhodanine derivatives (A1-10) have been synthesized and characterized by spectral data(IR, 1H-NMR, C13-NMR,and HR-MS).All the designed compounds were subjected to In-silico studies using Schrodinger softwareand evaluated for In-vitro antidiabetic activityby α-amylase and α-glucosidase inhibitory assays.Among the tested compoundsA5, A6 and A9 showed good activity when compared to the standard Acarbose. Also, Molecular dynamic (MD) simulations were conducted to evaluate the stability of the ligand-protein complex by the calculation of the root mean of square deviation (RMSD), root mean square fluctuation (RMSF), and solvent accessible surface area (SASA).

Identifying signatures of proteolytic stability and monomeric propensity in O-glycosylated insulin using molecular simulation

Insulin has been commonly adopted as a peptide drug to treat diabetes given its ability to facilitate the uptake of glucose from the blood. The development of oral insulin remains elusive over decades owing to its susceptibility to the enzymes in the gastrointestinal tract and poor permeability through the intestinal epithelium upon dimerization. Recent experimental studies have revealed that certain O-linked glycosylation patterns could enhance insulin’s proteolytic stability and reduce its dimerization propensity, but the understanding of such phenomena at the molecular level is still evasive. To address this challenge, we propose and test several structural determinants that could potentially in uence insulin’s proteolytic stability and dimerization propensity. We used these as the metrics to assess the properties of interest from 10 s aggregate molecular dynamics of each of 12 targeted insulin glyco-variants from multiple wild-type crystal structures. We found that glycan-involved hydrogen bonds and glycan-dimer occlusion were useful metrics predicting the proteolytic stability and dimerization propensity of insulin, as was in part the solvent accessible surface area of proteolytic sites, while other plausible metrics were not generally predictive. This work helps better explain how O-linked glycosylation in uences the proteolytic stability and monomeric propensity of insulin, illuminating a path towards rational molecular design of insulin glycoforms.

Mollusc-Derived Brominated Indoles for the Selective Inhibition of Cyclooxygenase: A Computational Expedition

Inflammation plays an important role in different chronic diseases. Brominated indoles derived from the Australian marine mollusk Dicathais orbita (D. orbita) are of interest for their anti-inflammatory properties. This study evaluates the binding mechanism and potentiality of several brominated indoles (tyrindoxyl sulfate, tyrindoleninone, 6-bromoisatin, and 6,6′-dibromoindirubin) against inflammatory mediators cyclooxygenases-1/2 (COX-1/2) using molecular docking, followed by molecular dynamics simulation, along with physicochemical, drug-likeness, pharmacokinetic (pk), and toxicokinetic (tk) properties. Molecular docking identified that these indole compounds are anchored, with the main amino acid residues, positioned in the binding pocket of the COX-1/2, required for selective inhibition. Moreover, the molecular dynamics simulation based on root mean square deviation (RMSD), radius of gyration (Rg), solvent accessible surface area (SASA), and root mean square fluctuation (RMSF) analyses showed that these natural brominated molecules transit rapidly to a progressive constant configuration during binding with COX-1/2 and seem to accomplish a consistent dynamic behavior by maintaining conformational stability and compactness. The results were comparable to the Food and Drug Administration (FDA)-approved selective COX inhibitor, aspirin. Furthermore, the free energy of binding for the compounds assessed by molecular mechanics–Poisson–Boltzmann surface area (MM–PBSA) confirmed the binding capacity of indoles towards COX-1/2, with suitable binding energy values except for the polar precursor tyrindoxyl sulfate (with COX-1). The physicochemical and drug-likeness analysis showed zero violations of Lipinski’s rule, and the compounds are predicted to have excellent pharmacokinetic profiles. These indoles are projected to be non-mutagenic and free from hepatotoxicity, with no inhibition of human ether-a-go–go gene (hERG) I inhibitors, and the oral acute toxicity LD50 in rats is predicted to be similar or lower than aspirin. Overall, this work has identified a plausible mechanism for selective COX inhibition by natural marine indoles as potential therapeutic candidates for the mitigation of inflammation.

Investigating Fungi-Derived Bioactive Molecules as Inhibitor of the SARS Coronavirus Papain Like Protease: Computational Based Study

Due to the rapid growth of the COVID-19 pandemic and its outcomes, developing a remedy to fight the predicament is critical. So far, it has infected more than 214,468,601 million people and caused the death of 4,470,969 million people according to the August 27, 2021, World Health Organization's (WHO) report. Several studies have been published on both computational and wet-lab approaches to develop antivirals for COVID-19, although there has been no success yet. However, the wet-lab approach is laborious, expensive, and time-consuming, and computational techniques have screened the activity of bioactive compounds from different sources with less effort and cost. For this investigation, we screened the binding affinity of fungi-derived bioactive molecules toward the SARS coronavirus papain-like protease (PLpro) by using computational approaches. Studies showed that protease inhibitors can be very effective in controlling virus-induced infections. Additionally, fungi represent a vast source of bioactive molecules, which could be potentially used for antiviral therapy. Fifty fungi-derived bioactive compounds were investigated concerning SARS-CoV-2 PLpro by using Auto Dock 4.2.1, Gromacs 2018. 2, ADMET, Swiss-ADME, FAF-Drugs 4.023, pKCSM, and UCLA-DOE server. From the list of the screened bioactive compounds, Dihydroaltersolanol C, Anthraquinone, Nigbeauvin A, and Catechin were selected with the Auto-Dock results of −8.68, −7.52, −10.46, and −10.58 Kcal/mol, respectively, based on their binding affinity compared to the reference drug. We presented the drug likeliness, toxicity, carcinogenicity, and mutagenicity of all compounds using ADMET analysis. They interacted with the amino acid residues, Gly163, Trp106, Ser111, Asp164, and Cys270, through hydrogen bonds. The root-mean-square deviation (RMSD), root-mean-square fluctuations (RMSF), solvent-accessible surface area (SASA), and radius of gyration (Rg) values revealed a stable interaction. From the overall analyses, we can conclude that Dihydroaltersolanol C, Anthraquinone, Nigbeauvin A, and Catechin are classified as promising candidates for PLpro, thus potentially useful in developing a medicine for COVID-19.

Screening of Potent Phytochemical Inhibitors Against SARS-CoV-2 Main Protease: An Integrative Computational Approach

Coronavirus disease 2019 (COVID-19) is a potentially lethal and devastating disease that has quickly become a public health threat worldwide. Due to its high transmission rate, many countries were forced to implement lockdown protocols, wreaking havoc on the global economy and the medical crisis. The main protease (Mpro) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the causative virus for COVID-19, represent an effective target for the development of a new drug/vaccine because it is well-conserved and plays a vital role in viral replication. Mpro inhibition can stop the replication, transcription as well as recombination of SARS-CoV-2 after the infection and thus can halt the formation of virus particles, making Mpro a viable therapeutic target. Here, we constructed a phytochemical dataset based on a rigorous literature review and explored the probability that various phytochemicals will bind with the main protease using a molecular docking approach. The top three hit compounds, medicagol, faradiol, and flavanthrin, had binding scores of −8.3, −8.6, and −8.8 kcal/mol, respectively, in the docking analysis. These three compounds bind to the active groove, consisting of His41, Cys45, Met165, Met49, Gln189, Thr24, and Thr190, resulting in main protease inhibition. Moreover, the multiple descriptors from the molecular dynamics simulation, including the root-mean-square deviation, root-mean-square fluctuation, solvent-accessible surface area, radius of gyration, and hydrogen bond analysis, confirmed the stable nature of the docked complexes. In addition, absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis confirmed a lack of toxicity or carcinogenicity for the screened compounds. Our computational analysis may contribute toward the design of an effective drug against the main protease of SARS-CoV-2.