PurificationDB: database of purification conditions for proteins.

The isolation of proteins of interest from cell lysates is an integral step to study protein structure and function. Liquid chromatography is a technique commonly used for protein purification, where the separation is performed by exploiting the differences in physical and chemical characteristics of proteins. The complex nature of proteins requires researchers to carefully choose buffers that maintain stability and activity of the protein while also allowing for appropriate interaction with chromatography columns. To choose the proper buffer, biochemists often search for reports of successful purification in the literature; however, they often encounter roadblocks such as lack of accessibility to journals, non-exhaustive specification of components and unfamiliar naming conventions. To overcome such issues, we present PurificationDB (https://purificationdatabase.herokuapp.com/), an open-access and user-friendly knowledge base that contains 4732 curated and standardized entries of protein purification conditions. Buffer specifications were derived from the literature using named-entity recognition techniques developed using common nomenclature provided by protein biochemists. PurificationDB also incorporates information associated with well-known protein databases: Protein Data Bank and UniProt. PurificationDB facilitates easy access to data on protein purification techniques and contributes to the growing effort of creating open resources that organize experimental conditions and data for improved access and analysis. Database URL https://purificationdatabase.herokuapp.com/.

Large-Scale Virtual Screening for the Discovery of SARS-CoV-2 Papain-like Protease (PLpro) Non-covalent Inhibitors.

The rapid global spread of the SARS-CoV-2 virus facilitated the development of novel direct-acting antiviral agents (DAAs). The papain-like protease (PLpro) has been proposed as one of the major SARS-CoV-2 targets for DAAs due to its dual role in processing viral proteins and facilitating the host's immune suppression. This dual role makes identifying small molecules that can effectively neutralize SARS-CoV-2 PLpro activity a high-priority task. However, PLpro drug discovery faces a significant challenge due to the high mobility and induced-fit effects in the protease's active site. Herein, we virtually screened the ZINC20 database with Deep Docking (DD) to identify prospective noncovalent PLpro binders and combined ultra-large consensus docking with two pharmacophore (ph4)-filtering strategies. The analysis of active compounds revealed their somewhat-limited diversity, likely attributed to the induced-fit nature of PLpro's active site in the crystal structures, and therefore, the use of rigid docking protocols poses inherited limitations. The top hits were assessed against recombinant viral proteins and live viruses, demonstrating desirable inhibitory activities. The best compound VPC-300195 (IC50: 15 µM) ranks among the top noncovalent PLpro inhibitors discovered through in silico methodologies. In the search for novel SARS-CoV-2 PLpro-specific chemotypes, the identified inhibitors could serve as diverse templates for the development of effective noncovalent PLpro inhibitors.

Ligand Binding Prediction Using Protein Structure Graphs and Residual Graph Attention Networks.

Computational prediction of ligand-target interactions is a crucial part of modern drug discovery as it helps to bypass high costs and labor demands of in vitro and in vivo screening. As the wealth of bioactivity data accumulates, it provides opportunities for the development of deep learning (DL) models with increasing predictive powers. Conventionally, such models were either limited to the use of very simplified representations of proteins or ineffective voxelization of their 3D structures. Herein, we present the development of the PSG-BAR (Protein Structure Graph-Binding Affinity Regression) approach that utilizes 3D structural information of the proteins along with 2D graph representations of ligands. The method also introduces attention scores to selectively weight protein regions that are most important for ligand binding. Results: The developed approach demonstrates the state-of-the-art performance on several binding affinity benchmarking datasets. The attention-based pooling of protein graphs enables identification of surface residues as critical residues for protein-ligand binding. Finally, we validate our model predictions against an experimental assay on a viral main protease (Mpro)-the hallmark target of SARS-CoV-2 coronavirus.