Exploring Molecular Contacts of MUC1 at CIN85 Binding Interface to Address Future Drug Design Efforts

The modulation of protein-protein interactions (PPIs) by small molecules represents a valuable strategy for pharmacological intervention in several human diseases. In this context, computer-aided drug discovery techniques offer useful resources to predict the network of interactions governing the recognition process between protein partners, thus furnishing relevant information for the design of novel PPI modulators. In this work, we focused our attention on the MUC1-CIN85 complex as a crucial PPI controlling cancer progression and metastasis. MUC1 is a transmembrane glycoprotein whose extracellular domain contains a variable number of tandem repeats (VNTRs) regions that are highly glycosylated in normal cells and under-glycosylated in cancer. The hypo-glycosylation fosters the exposure of the backbone to new interactions with other proteins, such as CIN85, that alter the intracellular signalling in tumour cells. Herein, different computational approaches were combined to investigate the molecular recognition pattern of MUC1-CIN85 PPI thus unveiling new structural information useful for the design of MUC1-CIN85 PPI inhibitors as potential anti-metastatic agents.     

The Use of Molecular Dynamics Simulation Method to Quantitatively Evaluate the Affinity between HBV Antigen T Cell Epitope Peptides and HLA-A Molecules

Chronic hepatitis B virus (HBV), a potentially life-threatening liver disease, makes people vulnerable to serious diseases such as cancer. T lymphocytes play a crucial role in clearing HBV virus, while the pathway depends on the strong binding of T cell epitope peptide and HLA. However, the experimental identification of HLA-restricted HBV antigenic peptides is extremely time-consuming. In this study, we provide a novel prediction strategy based on structure to assess the affinity between the HBV antigenic peptide and HLA molecule. We used residue scanning, peptide docking and molecular dynamics methods to obtain the molecular docking model of HBV peptide and HLA, and then adopted the MM-GBSA method to calculate the binding affinity of the HBV peptide–HLA complex. Overall, we collected 59 structures of HLA-A from Protein Data Bank, and finally obtained 352 numerical affinity results to figure out the optimal bind choice between the HLA-A molecules and 45 HBV T cell epitope peptides. The results were highly consistent with the qualitative affinity level determined by the competitive peptide binding assay, which confirmed that our affinity prediction process based on an HLA structure is accurate and also proved that the homologous modeling strategy for HLA-A molecules in this study was reliable. Hence, our work highlights an effective way by which to predict and screen for HLA-peptide binding that would improve the treatment of HBV infection.     

Peptide Derivatives of the Zonulin Inhibitor Larazotide (AT1001) as Potential Anti SARS-CoV-2: Molecular Modelling,Synthesis and Bioactivity Evaluation

A novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has been identified as the pathogen responsible for the outbreak of a severe, rapidly developing pneumonia (Coronavirus disease 2019, COVID-19). The virus enzyme, called 3CLpro or main protease (M^pro), is essential for viral replication, making it a most promising target for antiviral drug development. Recently, we adopted the drug repurposing as appropriate strategy to give fast response to global COVID-19 epidemic, by demonstrating that the zonulin octapeptide inhibitor AT1001 (Larazotide acetate) binds M^pro catalytic domain. Thus, in the present study we tried to investigate the antiviral activity of AT1001, along with five derivatives, by cell-based assays. Our results provide with the identification of AT1001 peptide molecular framework for lead optimization step to develop new generations of antiviral agents of SARS-CoV-2 with an improved biological activity, expanding the chance for success in clinical trials.     

Study of specific interactions in inclusion complexes of amine-terminated PAMAM dendrimer/flavonoids by experimental and computational methods

Polyamidoamine (PAMAM) dendrimers have a multifunctional structure, able to encapsulate molecules for pharmacological applications. We evaluated the specific interaction that govern the encapsulating and affinity of one group of natural and synthetic flavonoids into the G₅-PAMAM dendrimers. The complexation and capture percent of one flavonoid series into G₅-PAMAM dendrimers, under neutral and acid pH conditions, were studied through UV–Vis spectroscopy. Additionally, only three of the flavonoids (two synthetic and one natural) were studied by high-performance liquid chromatography (HPLC) and molecular dynamic (MD) simulation, at neutral pH to calculate the affinity constants (K_d) and binding free energies (ΔG_b). From spectroscopic results, we observed that the encapsulation was much more rapid at low pH than at neutral pH, which was attributed to a greater number of cavities inside the dendrimer. The MD simulations suggested that the more compact molecular structure at neutral pH reduces the capture kinetics. Finally, the relative binding free energies calculated using MD simulations showed the same tendency as the experimental data for the three complexes. These affinities appear to be due to a complex balance of different contributions, which cannot be attributed to hydrogen bonds or charge–charge interactions alone. Nevertheless, we suggest that a protocol including UV–Vis, HPLC, and MD simulation can be a powerful predictive tool to determine the affinity of drug binding to nanocarriers.     

Could MM-GBSA be accurate enough for calculation of absolute protein/ligand binding free energies?

Implicit solvation methods such as MM-GBSA, when applied to evaluating protein/ligand binding free energies, are widely believed to be accurate only for the estimation of relative binding free energies for a congeneric series of ligands. In this work, we show that the MM-GBSA flavor of Prime 3.0, VSGB-2.0, with a variable dielectric model and a novel energy function, could be approaching the accuracy required for evaluating absolute binding free energies, albeit, through a linear regression fit. The data-set used for validation includes 106 protein–ligand complexes that were carefully selected to control for variability in the affinity data as well as error in the modeled complexes. Through systematic analysis, we also quantify the degradation in the R² of fit between experimental and calculated values with either greater variability in the affinity data or an increase in error in the modeled protein/ligand complexes. Limitations for its application in drug discovery are discussed along with the identification of areas for future development.     

Trastuzumab-Peptide Interactions: Mechanism and Application in Structure-Based Ligand Design

Understanding of protein-ligand interactions and its influences on protein stability is necessary in the research on all biological processes and correlative applications, for instance, the appropriate affinity ligand design for the purification of bio-drugs. In this study, computational methods were applied to identify binding site interaction details between trastuzumab and its natural receptor. Trastuzumab is an approved antibody used in the treatment of human breast cancer for patients whose tumors overexpress the HER2 (human epidermal growth factor receptor 2) protein. However, rational design of affinity ligands to keep the stability of protein during the binding process is still a challenge. Herein, molecular simulations and quantum mechanics were used on protein-ligand interaction analysis and protein ligand design. We analyzed the structure of the HER2-trastuzumab complex by molecular dynamics (MD) simulations. The interaction energies of the mutated peptides indicate that trastuzumab binds to ligand through electrostatic and hydrophobic interactions. Quantitative investigation of interactions shows that electrostatic interactions play the most important role in the binding of the peptide ligand. Prime/MM-GBSA calculations were carried out to predict the binding affinity of the designed peptide ligands. A high binding affinity and specificity peptide ligand is designed rationally with equivalent interaction energy to the wild-type octadecapeptide. The results offer new insights into affinity ligand design.     

Molecular dynamics simulations studies and free energy analysis on inhibitors of MDM2–p53 interaction

The oncoprotein MDM2 (murine double minute 2) negatively regulates the activity and stability of tumor suppressor p53. Inactivation of the MDM2–p53 interaction by potent inhibitors offers new possibilities for anticancer therapy. Molecular dynamics (MD) simulations were performed on three inhibitors–MDM2 complexes to investigate the stability and structural transitions. Simulations show that the backbone of MDM2 maintains stable during the whole time. However, slightly structural changes of inhibitors and MDM2 are observed. Furthermore, the molecular mechanics generalized Born surface area (MM-GBSA) approach was introduced to analyze the interactions between inhibitors and MDM2. The results show that binding of inhibitor pDIQ to MDM2 is significantly stronger than that of pMI and pDI to MDM2. The side chains of residues have more contribution than backbone of residues in energy decomposition. The structure–affinity analyses show that L54, I61, M62, Y67, Q72, H73 and V93 produce important interaction energy with inhibitors. The residue W/Y22′ is also very important to the interaction between the inhibitors and MDM2. The three-dimensional structures at different times indicate that the mobility of Y100 influences on the binding of inhibitors to MDM2, and its change has important role in conformations of inhibitors and MDM2.     

Improved Binding Affinity of Omicron’s Spike Protein for the Human Angiotensin-Converting Enzyme 2 Receptor Is the Key behind Its Increased Virulence

The new variant of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2), Omicron, has been quickly spreading in many countries worldwide. Compared to the original virus, Omicron is characterized by several mutations in its genomic region, including the spike protein’s receptor-binding domain (RBD). We have computationally investigated the interaction between the RBD of both the wild type and Omicron variant of SARS-CoV-2 with the human angiotensin-converting enzyme 2 (hACE2) receptor using molecular dynamics and molecular mechanics-generalized Born surface area (MM-GBSA)-based binding free energy calculations. The mode of the interaction between Omicron’s RBD with the hACE2 receptor is similar to the original SARS-CoV-2 RBD except for a few key differences. The binding free energy difference shows that the spike protein of Omicron has an increased affinity for the hACE2 receptor. The mutated residues in the RBD showed strong interactions with a few amino acid residues of hACE2. More specifically, strong electrostatic interactions (salt bridges) and hydrogen bonding were observed between R493 and R498 residues of the Omicron RBD with D30/E35 and D38 residues of the hACE2, respectively. Other mutated amino acids in the Omicron RBD, e.g., S496 and H505, also exhibited hydrogen bonding with the hACE2 receptor. A pi-stacking interaction was also observed between tyrosine residues (RBD-Tyr501: hACE2-Tyr41) in the complex, which contributes majorly to the binding free energies and suggests that this is one of the key interactions stabilizing the formation of the complex. The resulting structural insights into the RBD:hACE2 complex, the binding mode information within it, and residue-wise contributions to the free energy provide insight into the increased transmissibility of Omicron and pave the way to design and optimize novel antiviral agents.