Computational modeling of cyclic peptide inhibitor-MDM2/MDMX binding through global docking and Gaussian accelerated molecular dynamics simulations

Computational-based drug design of novel small molecules targeting p53-MDMX interaction

The regulation of the p53 tumor suppressor pathway is critically dependent on the activity of Murine Double Minute 2 (MDM2) and Murine Double Minute X (MDMX) proteins. In certain types of cancer cells, excessive amount of MDMX can poly-ubiquitinate p53, which can result in its degradation, leading to a subsequent reduction in the levels of this protein. Therefore, the design of small-molecule inhibitors targeting the MDMX-p53 interaction has emerged as a promising strategy for cancer therapy. In this study, we employed computational techniques including pharmacophore modeling and molecular docking to identify three potential small molecule inhibitors (CID_25094615, CID_137634453, and CID_25094344) of the MDMX-p53 interaction from a PubChem database. Molecular dynamics of 100000 ps were conducted to assess the stability of the MDMX-inhibitor complexes. Our results showed that all three compounds exhibit stable binding with MDMX, with significantly lower root mean square deviation (RMSD) and fluctuation (RMSF) values than the control ligand, indicating superior stability. Additionally, the three compounds exhibit stronger intermolecular hydrogen bond (HBOND) interactions compared to the control, suggesting stronger stability. Overall, our findings highlight the potential of these compounds as lead candidates for the development of novel anticancer agents that target the MDMX-p53 interaction.Communicated by Ramaswamy H. Sarma.

Insights into the Interaction Mechanisms of Peptide and Non-Peptide Inhibitors with MDM2 Using Gaussian-Accelerated Molecular Dynamics Simulations and Deep Learning

Inhibiting MDM2-p53 interaction is considered an efficient mode of cancer treatment. In our current study, Gaussian-accelerated molecular dynamics (GaMD), deep learning (DL), and binding free energy calculations were combined together to probe the binding mechanism of non-peptide inhibitors K23 and 0Y7 and peptide ones PDI6W and PDI to MDM2. The GaMD trajectory-based DL approach successfully identified significant functional domains, predominantly located at the helixes α2 and α2', as well as the β-strands and loops between α2 and α2'. The post-processing analysis of the GaMD simulations indicated that inhibitor binding highly influences the structural flexibility and collective motions of MDM2. Calculations of molecular mechanics-generalized Born surface area (MM-GBSA) and solvated interaction energy (SIE) not only suggest that the ranking of the calculated binding free energies is in agreement with that of the experimental results, but also verify that van der Walls interactions are the primary forces responsible for inhibitor-MDM2 binding. Our findings also indicate that peptide inhibitors yield more interaction contacts with MDM2 compared to non-peptide inhibitors. Principal component analysis (PCA) and free energy landscape (FEL) analysis indicated that the piperidinone inhibitor 0Y7 shows the most pronounced impact on the free energy profiles of MDM2, with the piperidinone inhibitor demonstrating higher fluctuation amplitudes along primary eigenvectors. The hot spots of MDM2 revealed by residue-based free energy estimation provide target sites for drug design toward MDM2. This study is expected to provide useful theoretical aid for the development of selective inhibitors of MDM2 family members.

Molecular Dynamics Simulations in Protein-Protein Docking

Concerted interactions between all the cell components form the basis of biological processes. Protein-protein interactions (PPIs) constitute a tremendous part of this interaction network. Deeper insight into PPIs can help us better understand numerous diseases and lead to the development of new diagnostic and therapeutic strategies. PPI interfaces, until recently, were considered undruggable. However, it is now believed that the interfaces contain "hot spots," which could be targeted by small molecules. Such a strategy would require high-quality structural data of PPIs, which are difficult to obtain experimentally. Therefore, in silico modeling can complement or be an alternative to in vitro approaches. There are several computational methods for analyzing the structural data of the binding partners and modeling of the protein-protein dimer/oligomer structure. The major problem with in silico structure prediction of protein assemblies is obtaining sufficient sampling of protein dynamics. One of the methods that can take protein flexibility and the effects of the environment into account is Molecular Dynamics (MD). While sampling of the whole protein-protein association process with plain MD would be computationally expensive, there are several strategies to harness the method to PPI studies while maintaining reasonable use of resources. This chapter reviews known applications of MD in the PPI investigation workflows.

A Review: The Antiviral Activity of Cyclic Peptides

In the design and development of therapeutic agents, macromolecules with restricted structures have stronger competitive edges than linear biological entities since cyclization can overcome the limitations of linear structures. The common issues of linear peptides include susceptibility to degradation of the peptidase enzyme, off-target effects, and necessity of routine dosing, leading to instability and ineffectiveness. The unique conformational constraint of cyclic peptides provides a larger surface area to interact with the target at the same time, improving the membrane permeability and in vivo stability compared to their linear counterparts. Currently, cyclic peptides have been reported to possess various activities, such as antifungal, antiviral and antimicrobial activities. To date, there is emerging interest in cyclic peptide therapeutics, and increasing numbers of clinically approved cyclic peptide drugs are available on the market. In this review, the medical significance of cyclic peptides in the defence against viral infections will be highlighted. Except for chikungunya virus, which lacks specific antiviral treatment, all the viral diseases targeted in this review are those with effective treatments yet with certain limitations to date. Thus, strategies and approaches to optimise the antiviral effect of cyclic peptides will be discussed along with their respective outcomes. Apart from isolated naturally occurring cyclic peptides, chemically synthesized or modified cyclic peptides with antiviral activities targeting coronavirus, herpes simplex viruses, human immunodeficiency virus, Ebola virus, influenza virus, dengue virus, five main hepatitis viruses, termed as type A, B, C, D and E and chikungunya virus will be reviewed herein.

Graphical Abstract

A new chiral stationary phase based on noscapine: Synthesis, enantioseparation, and docking study

Noscapine is an isolated compound from the opium poppy, with distinctive chiral structure and chemistry, interacts with other compounds due to having multiple π-acceptors, hydrogen bond acceptors, and ionic sites. Therefore, it has promising applicability for the enantioselective separation of a wide range of polar, acidic, basic, and neutral compounds. A new noscapine derivative chiral stationary phase (ND-CSP) has been synthesized by consecutive N-demethylation, reduction, and N-propargylation of noscapine followed by attachment of a solid epoxy-functionalized silica bed through the 1,3-dipolar Huisgen cycloaddition. The noscapine derivative-based stationary phase provides a considerable surface coverage, which is greater than some commercial CSPs and can validate better enantioresolution performance. The major advantages inherent to this chiral selector are stability, reproducibility after more than 200 tests, and substantial loading capacity. The characterization by Fourier transform infrared (FTIR) spectroscopy and elemental analysis indicated successful functionalization of the silica surface. Chromatographic method conditions like flow rate and mobile phase composition for enantioseparation of various compounds such as warfarin, propranolol, mandelic acid, and a sulfanilamide derivative were optimized. Comparing the experimental results with docking data revealed a clear correlation between the calculated binding energy of ND-CSP and each enantiomer with the resolution of enantiomer peaks.

Binding Analysis Using Accelerated Molecular Dynamics Simulations and Future Perspectives

Biomolecular recognition such as binding of small molecules, nucleic acids, peptides and proteins to their target receptors plays key roles in cellular function and has been targeted for therapeutic drug design. Molecular dynamics (MD) is a computational approach to analyze these binding processes at an atomistic level, which provides valuable understandings of the mechanisms of biomolecular recognition. However, the rather slow biomolecular binding events often present challenges for conventional MD (cMD), due to limited simulation timescales (typically over hundreds of nanoseconds to tens of microseconds). In this regard, enhanced sampling methods, particularly accelerated MD (aMD), have proven useful to bridge the gap and enable all-atom simulations of biomolecular binding events. Here, we will review the recent method developments of Gaussian aMD (GaMD), ligand GaMD (LiGaMD) and peptide GaMD (Pep-GaMD), which have greatly expanded our capabilities to simulate biomolecular binding processes. Spontaneous binding of various biomolecules to their receptors has been successfully simulated by GaMD. Microsecond LiGaMD and Pep-GaMD simulations have captured repetitive binding and dissociation of small-molecule ligands and highly flexible peptides, and thus enabled ligand/peptide binding thermodynamics and kinetics calculations. We will also present relevant application studies in simulations of important drug targets and future perspectives for rational computer-aided drug design.