In Silico Study of Pacific oyster Antiviral Polypeptides as Potential Inhibitory Compounds for SARS-CoV-2 Main Protease

Background: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a novel pathogen that has triggered a pneumonia outbreak, and despite the measures, the pandemic still continues to occur. Objectives: The molecular docking analysis was used to test whether the human immunodeficiency virus 1 (HIV-1) protease inhibitory peptides. These marine polypeptides were isolated from the hydrolysate of Pacific oyster. Methods: Molecular docking process was performed using Molegro Virtual Docker software. The protein data bank file of the crystal structure of COVID-19 main protease in complex with an inhibitor N3 (ID 6LU7) was obtained from the PubChem data source. After preparing protein and removing water and internal ligand, the major cavity was selected for the next step, the docking procedure. Afterward, the MolDock score, Rerank score, Total interaction energy (between energy), and HBond item were calculated. The Remdesivir was used as a positive control in the docking project. Results: The results of the docking step were evaluated based on several bioinformatics docking scores, including MolDock score, Rerank score, Total interaction energy (between energy), and HBond. The hydrogen bond of remdesivir was -6.03673, and Leu-Leu-Glu-Tyr-Ser-Ileu polypeptide was -6.44185. The Rerank score of remdesivir was -98.9254 and for Leu-Leu-Glu-Tyr-Ser-Ileu polypeptide was -107.821. Of the two screened Pacific oyster polypeptides, the score of Leu-Leu-Glu-Tyr-Ser-Ileu ligand was higher than remdesivir. Conclusions: This study demonstrated that Pacific oyster compounds may have the potency to be evolved as an anti-COVID-19 main protease drug to fight against the novel coronavirus; however, preclinical and clinical trials are needed for further experimental and/or clinical scientific validation.

D+: software for high-resolution hierarchical modeling of solution X-ray scattering from complex structures

This paper presents the computer program D+ (https://scholars.huji.ac.il/uriraviv/book/d-0), where the reciprocal-grid (RG) algorithm is implemented. D+ efficiently computes, at high-resolution, the X-ray scattering curves from complex structures that are isotropically distributed in random orientations in solution. Structures are defined in hierarchical trees in which subunits can be represented by geometric or atomic models. Repeating subunits can be docked into their assembly symmetries, describing their locations and orientations in space. The scattering amplitude of the entire structure can be calculated by computing the amplitudes of the basic subunits on 3D reciprocal-space grids, moving up in the hierarchy, calculating the RGs of the larger structures, and repeating this process for all the leaves and nodes of the tree. For very large structures (containing over 100 protein subunits), a hybrid method can be used to avoid numerical artifacts. In the hybrid method, only grids of smaller subunits are summed and used as subunits in a direct computation of the scattering amplitude. D+ can accurately analyze both small- and wide-angle solution X-ray scattering data. This article describes how D+ applies the RG algorithm, accounts for rotations and translations of subunits, processes atomic models, accounts for the contribution of the solvent as well as the solvation layer of complex structures in a scalable manner, writes and accesses RGs, interpolates between grid points, computes numerical integrals, enables the use of scripts to define complicated structures, applies fitting algorithms, accounts for several coexisting uncorrelated populations, and accelerates computations using GPUs. D+ may also account for different X-ray energies to analyze anomalous solution X-ray scattering data. An accessory tool that can identify repeating subunits in a Protein Data Bank file of a complex structure is provided. The tool can compute the orientation and translation of repeating subunits needed for exploiting the advantages of the RG algorithm in D+. A Python wrapper (https://scholars.huji.ac.il/uriraviv/book/python-api) is also available, enabling more advanced computations and integration of D+ with other computational tools. Finally, a large number of tests are presented. The results of D+ are compared with those of other programs when possible, and the use of D+ to analyze solution scattering data from dynamic microtubule structures with different protofilament number is demonstrated. D+ and its source code are freely available for academic users and developers (https://bitbucket.org/uriraviv/public-dplus/src/master/).

ANODE: anomalous and heavy-atom density calculation

The new programANODEestimates anomalous or heavy-atom density by reversing the usual procedure for experimental phase determination by methods such as single- and multiple-wavelength anomalous diffraction and single isomorphous replacement anomalous scattering. Instead of adding a phase shift to the heavy-atom phases to obtain a starting value for the native protein phase, this phase shift is subtracted from the native phase to obtain the heavy-atom substructure phase. The required native phase is calculated from the information in a Protein Data Bank file of the structure. The resulting density enables even very weak anomalous scatterers such as sulfur to be located. Potential applications include the identification of unknown atoms and the validation of molecular replacement solutions.