Structures of macromolecules in their native state provide unique unambiguous insights into their functions. Cryo-electron tomography combined with subtomogram averaging demonstrated the power to solve such structures in situ at resolutions in the range of 3 Angstrom for some macromolecules. In order to be applicable to structural determination of the majority of macromolecules observable in cells in limited amounts, processing of tomographic data has to be performed in a high-throughput manner. Here we present TomoBEAR - a modular configurable workflow engine for streamlined processing of cryo-electron tomographic data for subtomogram averaging. TomoBEAR combines commonly used cryo-EM packages and reasonable presets to provide a transparent "white box" for data management and processing. We demonstrate applications of TomoBEAR to two datasets of purified proteins and to a membrane protein RyR1 in a membrane and demonstrate the ability to produce high resolution with minimal human intervention. TomoBEAR is an open-source and extendable package, it will accelerate the adoption of in situ structural biology by cryo-ET.

Over the past three years, significant progress has been made in the development of novel promising drug candidates against COVID-19. However, SARS-CoV-2 mutations resulting in the emergence of new viral strains that can be resistant to the drugs used currently in the clinic necessitate the development of novel potent and broad therapeutic agents targeting different vulnerable spots of the viral proteins. In this study, two deep learning generative models were developed and used in combination with molecular modeling tools for de novo design of small molecule compounds that can inhibit the catalytic activity of SARS-CoV-2 main protease (Mpro), an enzyme critically important for mediating viral replication and transcription. As a result, the seven best scoring compounds that exhibited low values of binding free energy comparable with those calculated for two potent inhibitors of Mpro, via the same computational protocol, were selected as the most probable inhibitors of the enzyme catalytic site. In light of the data obtained, the identified compounds are assumed to present promising scaffolds for the development of new potent and broad-spectrum drugs inhibiting SARS-CoV-2 Mpro, an attractive therapeutic target for anti-COVID-19 agents.

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Two generative deep learning models have been developed for the computer-aided design of potential inhibitors of the SARS-CoV-2 main protease (MPro), an enzyme critically important for the virus replication and transcription, and, therefore, presenting a promising target for the design of effective antiviral drugs. To solve this problem, we formed a training library of small molecules containing structural elements capable of providing specific and effective interactions of potential ligands with the SARS-CoV-2 MPro catalytic site. The architecture of generative models was developed and implemented to generate new high-affinity ligands of this functionally important SARS-CoV-2 protein. The neural network was trained and tested on the compounds from the training library, and the results of training and operation in two different generation modes were evaluated. The use of generative models in conjunction with the molecular docking demonstrated their great potential for filling the unexplored regions of the chemical space with novel molecules with pre-defined properties, which is confirmed by the obtained results according to which out of 4805 compounds generated by the neural network only one compound was present in the original data set.