Evolutionary algorithm-based generation of optimum peptide sequences with dengue virus inhibitory activity

Background: There is currently no effective dengue virus (DENV) therapeutic. We aim to develop a genetic algorithm-based framework for the design of peptides with possible DENV inhibitory activity. Methods & results: A Python-based tool (denominated AutoPepGEN) based on a DENV support vector machine classifier as the objective function was implemented. AutoPepGEN was applied to the design of three- to seven-amino acid sequences and ten peptides were selected. Peptide–protease (DENV) docking and Molecular Mechanics–Generalized Born Surface Area calculations were performed for the selected sequences and favorable binding energies were observed. Conclusion: It is hoped that AutoPepGEN will serve as an in silico alternative to the experimental design of positional scanning combinatorial libraries, known to be prone to a combinatorial explosion. AutoPepGEN is available at: https://github.com/sjbarigye/AutoPepGEN .

Neural network technology for identifying defect sizes in half-plane based on time and positional scanning

Introduction. The selected research topic urgency is due to the need for a quick assessment of the condition and reliability of materials used in various designs. The work objective was to study parameters of the influence of the defect on the response of the surface of the medium to the shock effect. The solution to the inverse problem of restoring the radius of a defect is based on the combination of a computational approach and the use of artificial neural networks (ANN). The authors have developed a technique for restoring the parameters of a defect based on the computational modeling and ANN. Materials and Methods. The problem is solved in the flat setting through the finite element method (FEM). In this paper, we used the linear equations of the elasticity theory with allowance for energy dissipation. The finite element method implemented in the ANSYS package was used as a method for solving the boundary value problem. MATLAB complex was used as a simulation of the application process (ANN). Results. A finite element model of a layered structure has been developed in a flat formulation of the problem in the ANSYS package. The problem of determining unsteady vibrations under pulsed loading for different radius variations of the defect is solved. Positional scanning of the research object is applied. Graphical dependences of the vibration amplitudes of points on the surface on the defect radius are plotted. Discussion and Conclusions. As a result of studying the dependences of vibration responses on the defect radius, the authors have developed an approach to restore this parameter in a flat structure based on a combination of the FEM and ANN. The research has shown that the amount of data used is sufficient for successful training of the constructed ANN model and identification of a hidden defect in the structure.

Activity profiling and crystal structures of inhibitor-bound SARS-CoV-2 papain-like protease: A framework for anti–COVID-19 drug design

Viral papain-like cysteine protease (PLpro, NSP3) is essential for SARS-CoV-2 replication and represents a promising target for the development of antiviral drugs. Here, we used a combinatorial substrate library and performed comprehensive activity profiling of SARS-CoV-2 PLpro. On the scaffold of the best hits from positional scanning, we designed optimal fluorogenic substrates and irreversible inhibitors with a high degree of selectivity for SARS PLpro. We determined crystal structures of two of these inhibitors in complex with SARS-CoV-2 PLpro that reveals their inhibitory mechanisms and provides a molecular basis for the observed substrate specificity profiles. Last, we demonstrate that SARS-CoV-2 PLpro harbors deISGylating activity similar to SARSCoV-1 PLpro but its ability to hydrolyze K48-linked Ub chains is diminished, which our sequence and structure analysis provides a basis for. Together, this work has revealed the molecular rules governing PLpro substrate specificity and provides a framework for development of inhibitors with potential therapeutic value or drug repurposing.

Activity profiling and structures of inhibitor-bound SARS-CoV-2-PLpro protease provides a framework for anti-COVID-19 drug design

In December 2019, the first cases of a novel coronavirus infection causing COVID-19 were diagnosed in Wuhan, China. Viral Papain-Like cysteine protease (PLpro, NSP3) is essential for SARS-CoV-2 replication and represents a promising target for the development of antiviral drugs. Here, we used a combinatorial substrate library containing natural and a wide variety of nonproteinogenic amino acids and performed comprehensive activity profiling of SARS-CoV-2-PLpro. On the scaffold of best hits from positional scanning we designed optimal fluorogenic substrates and irreversible inhibitors with a high degree of selectivity for SARS PLpro variants versus other proteases. We determined crystal structures of two of these inhibitors (VIR250 and VIR251) in complex with SARS-CoV-2-PLpro which reveals their inhibitory mechanisms and provides a structural basis for the observed substrate specificity profiles. Lastly, we demonstrate that SARS-CoV-2-PLpro harbors deISGylating activities similar to SARS-CoV-1-PLpro but its ability to hydrolyze K48-linked Ub chains is diminished, which our sequence and structure analysis provides a basis for. Altogether this work has revealed the molecular rules governing PLpro substrate specificity and provides a framework for development of inhibitors with potential therapeutic value or drug repositioning.

Leveraging peptide substrate libraries to design inhibitors of bacterial Lon protease

Lon is a widely-conserved housekeeping protease found in all domains of life. Bacterial Lon is involved in the recovery from various types of stress, including tolerance to fluoroquinolone antibiotics, and is linked to pathogenesis in a number of organisms. However, detailed functional studies of Lon have been limited by the lack of selective, cell-permeable inhibitors. Here we describe the use of positional scanning libraries of hybrid peptide substrates to profile the primary sequence specificity of bacterial Lon. In addition to identifying optimal natural amino acid binding preferences, we identified several non-natural residues that were leveraged to develop optimal peptide substrates as well as a potent peptidic boronic acid inhibitor of Lon. Treatment ofE. coliwith this inhibitor promotes UV-induced filamentation and reduces tolerance to ciprofloxacin, phenocopying establishedlon-deletion phenotypes. It is also non-toxic to mammalian cells due to its increased selectivity for Lon over the proteasome. Our results provide new insight into the primary substrate specificity of Lon and identify substrates and an inhibitor that will serve as useful tools for dissecting the diverse cellular functions of Lon.

Identification of Bis-Cyclic Guanidines as Antiplasmodial Compounds from Positional Scanning Mixture-Based Libraries

The screening of more than 30 million compounds derived from 81 small molecule libraries built on 81 distinct scaffolds identified pyrrolidine bis-cyclic guanidine library (TPI-1955) to be one of the most active and selective antiplasmodial libraries. The screening of the positional scanning library TPI-1955 arranged on four sets of sublibraries (26 + 26 + 26 + 40), totaling 120 samples for testing provided information about the most important groups of each variable position in the TPI-1955 library containing 738,192 unique compounds. The parallel synthesis of the individual compounds derived from the deconvolution of the positional scanning library led to the identification of active selective antiplasmodial pyrrolidine bis-cyclic guanidines.