Could Dermaseptin Analogue be a Competitive Inhibitor for ACE2 Towards Binding with Viral Spike Protein Causing COVID19?: Computational Investigation

Shaping the Future of Antimicrobial Therapy: Harnessing the Power of Antimicrobial Peptides in Biomedical Applications

Antimicrobial peptides (AMPs) have emerged as a promising class of bioactive molecules with the potential to combat infections associated with medical implants and biomaterials. This review article aims to provide a comprehensive analysis of the role of antimicrobial peptides in medical implants and biomaterials, along with their diverse clinical applications. The incorporation of AMPs into various medical implants and biomaterials has shown immense potential in mitigating biofilm formation and preventing implant-related infections. We review the latest advancements in biomedical sciences and discuss the AMPs that were immobilized successfully to enhance their efficacy and stability within the implant environment. We also highlight successful examples of AMP coatings for the treatment of surgical site infections (SSIs), contact lenses, dental applications, AMP-incorporated bone grafts, urinary tract infections (UTIs), medical implants, etc. Additionally, we discuss the potential challenges and prospects of AMPs in medical implants, such as effectiveness, instability and implant-related complications. We also discuss strategies that can be employed to overcome the limitations of AMP-coated biomaterials for prolonged longevity in clinical settings.

Probing the competitive inhibitor efficacy of frog-skin alpha helical AMPs identified against ACE2 binding to SARS-CoV-2 S1 spike protein as therapeutic scaffold to prevent COVID-19

In COVID-19 infection, the SARS-CoV-2 spike protein S1 interacts to the ACE2 receptor of human host, instigating the viral infection. To examine the competitive inhibitor efficacy of broad spectrum alpha helical AMPs extracted from frog skin, a comparative study of intermolecular interactions between viral S1 and AMPs was performed relative to S1-ACE2p interactions. The ACE2 binding region with S1 was extracted as ACE2p from the complex for ease of computation. Surprisingly, the Spike-Dermaseptin-S9 complex had more intermolecular interactions than the other peptide complexes and importantly, the S1-ACE2p complex. We observed how atomic displacements in docked complexes impacted structural integrity of a receptor-binding domain in S1 through conformational sampling analysis. Notably, this geometry-based sampling approach confers the robust interactions that endure in S1-Dermaseptin-S9 complex, demonstrating its conformational transition. Additionally, QM calculations revealed that the global hardness to resist chemical perturbations was found more in Dermaseptin-S9 compared to ACE2p. Moreover, the conventional MD through PCA and the torsional angle analyses indicated that Dermaseptin-S9 altered the conformations of S1 considerably. Our analysis further revealed the high structural stability of S1-Dermaseptin-S9 complex and particularly, the trajectory analysis of the secondary structural elements established the alpha helical conformations to be retained in S1-Dermaseptin-S9 complex, as substantiated by SMD results. In conclusion, the functional dynamics proved to be significant for viral Spike S1 and Dermaseptin-S9 peptide when compared to ACE2p complex. Hence, Dermaseptin-S9 peptide inhibitor could be a strong candidate for therapeutic scaffold to prevent infection of SARS-CoV-2.

Structural localization of pathogenic mutations in the central nucleotide-binding domain (NBD) of nucleotide-binding oligomerization domain-2 (NOD2) protein and their inference in inflammatory disorders

The human NBD domain which is centrally located in the NOD2 protein displays an essential role in oligomerization and initiates the immune response via CARD-RIPK2 interaction. The mutations associated with the NBD domain have been largely implicated in inflammatory disorders such as Blau syndrome and sarcoidosis. This study aims to determine the structural and phenotypic effect of a lethal mutation that occurs in the NBD domain which has an axiomatic impact on protein dysfunction. Initially, the most deleterious missense mutations were screened through various in silico analysis. Out of 33 variants, I-Mutant 3.0, SIFT, PolyPhen 2, Align GVGD, PHD SNP and SNP&GO have statistically identified 5 variants (R42W, D90E, E91K, G189D & W198L) as less stable, deleterious and damaging. Our predicted models have paved the way to understand the various structural properties such as physiochemical, secondary structural arrangements and stabilizing residues in folding associated with the native and mutant NBD domain especially of the functionally important regions. From the aforementioned results, R42W and G189D were found to be the more predominant among the mutants. Precisely, through molecular simulation, we have strongly justified the significant conformational disruption of R42W and G189D through the stabilization factors, folding and essential dynamics. Conclusively, these regions (α₃41-44, α₁₃185-191 and β₆133-143β₇) seem to adopt such structures that are not conducive to wild-type-like functionality. Our prediction and validation of lethal mutations based on structural stability may be useful for conducting experimental studies in detail to uncover the protein deregulation leading to inflammatory disorders.

Perturbation of ACE2 Structural Ensembles by SARS-CoV-2 Spike Protein Binding

The human ACE2 enzyme serves as a critical first recognition point of coronaviruses, including SARS-CoV-2. In particular, the extracellular domain of ACE2 interacts directly with the S1 tailspike protein of the SARS-CoV-2 virion through a broad protein-protein interface. Although this interaction has been characterized by X-ray crystallography, these structures do not reveal significant differences in the ACE2 structure upon S1 protein binding. In this work, using several all-atom molecular dynamics simulations, we show persistent differences in the ACE2 structure upon binding. These differences are determined with the linear discriminant analysis (LDA) machine learning method and validated using independent training and testing datasets, including long trajectories generated by D. E. Shaw Research on the Anton 2 supercomputer. In addition, long trajectories for 78 potent ACE2-binding compounds, also generated by D. E. Shaw Research, were projected onto the LDA classification vector in order to determine whether the ligand-bound ACE2 structures were compatible with S1 protein binding. This allows us to predict which compounds are "apo-like" versus "complex-like" and to pinpoint long-range ligand-induced allosteric changes in the ACE2 structure.