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.

Predicting cryptic ligand binding sites based on normal modes guided conformational sampling.

To greatly expand the druggable genome, fast and accurate predictions of cryptic sites for small molecules binding in target proteins are in high demand. In this study, we have developed a fast and simple conformational sampling scheme guided by normal modes solved from the coarse-grained elastic models followed by atomistic backbone refinement and side-chain repacking. Despite the observations of complex and diverse conformational changes associated with ligand binding, we found that simply sampling along each of the lowest 30 modes is near optimal for adequately restructuring cryptic sites so they can be detected by existing pocket finding programs like fpocket and concavity. We further trained machine-learning protocols to optimize the combination of the sampling-enhanced pocket scores with other dynamic and conservation scores, which only slightly improved the performance. As assessed based on a training set of 84 known cryptic sites and a test set of 14 proteins, our method achieved high accuracy of prediction (with area under the receiver operating characteristic curve >0.8) comparable to the CryptoSite server. Compared with CryptoSite and other methods based on extensive molecular dynamics simulation, our method is much faster (1-2 hours for an average-size protein) and simpler (using only pocket scores), so it is suitable for high-throughput processing of large datasets of protein structures at the genome scale.