DINC-COVID: A webserver for ensemble docking with flexible SARS-CoV-2 proteins

Ensemble docking based virtual screening of SARS-CoV-2 main protease inhibitors

During the first years of COVID-19 pandemic, X-ray structures of the coronavirus drug targets were acquired at an unprecedented rate, giving hundreds of PDB depositions in less than a year. The main protease (Mpro) of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) is the primary validated target of direct-acting antivirals. The selection of the optimal ensemble of structures of Mpro for the docking-driven virtual screening campaign was thus non-trivial and required a systematic and automated approach. Here we report a semi-automated active site RMSD based procedure of ensemble selection from the SARS-CoV-2 Mpro crystallographic data and virtual screening of its inhibitors. The procedure was compared with other approaches to ensemble selection and validated with the help of hand-picked and peer-reviewed activity-annotated libraries. Prospective virtual screening of non-covalent Mpro inhibitors resulted in a new chemotype of thienopyrimidinone derivatives with experimentally confirmed enzyme inhibition.

Comparative docking and molecular dynamics studies of molnupiravir (EIDD-2801): implications for novel mechanisms of action on influenza and SARS-CoV-2 protein targets

Molnupiravir (EIDD-2801) (MLN) is an oral antiviral drug for COVID-19 treatment, being integrated into viral RNA through RNA-dependent RNA polymerase (RdRp). Upon ingestion, MLN is transformed into two active metabolites: β-d-N4-hydroxycytidine (NHC) (EIDD-1931) in the host plasma, and EIDD-1931-triphosphate (MTP) within the host cells. However, recent studies provide increasing evidence of MLN's interactions with off-target proteins beyond the viral genome, suggesting that the complete mechanisms of action of MLN remain unclear. The aim of this study was therefore to investigate the molecular interactions of MLN in the form of NHC and MTP with the non-RNA structural components of avian influenza (hemagglutinin, neuraminidase) and SARS-CoV-2 (spike glycoprotein, Mpro, and RdRp) viruses and to elucidate whether these two metabolites possess the ability to form stable complexes with these major viral components. Molecular docking of NHC and MTP was performed using AutoDock 4.2.6 and the obtained protein-drug complexes were submitted to 200-ns molecular dynamics simulations in triplicate with subsequent free energy calculations using GROMACS. Docking scores, molecular dynamics and MM/GBSA results showed that MTP was tightly bound within the active site of SARS-CoV-2 RdRp and remained highly stable throughout the 200-ns simulations. Besides, it was also shown that NHC and MTP formed moderately-to-highly stable molecular complexes with off-target receptors hemagglutinin, neuraminidase and Mpro, but rather weak interactions with spike glycoprotein. Our computational findings suggest that NHC and MTP may directly inhibit these receptors, and propose that additional studies on the off-target effects of MLN, i.e. real-time protein binding assays, should be performed.Communicated by Ramaswamy H. Sarma.

EnGens: a computational framework for generation and analysis of representative protein conformational ensembles

Proteins are dynamic macromolecules that perform vital functions in cells. A protein structure determines its function, but this structure is not static, as proteins change their conformation to achieve various functions. Understanding the conformational landscapes of proteins is essential to understand their mechanism of action. Sets of carefully chosen conformations can summarize such complex landscapes and provide better insights into protein function than single conformations. We refer to these sets as representative conformational ensembles. Recent advances in computational methods have led to an increase in the number of available structural datasets spanning conformational landscapes. However, extracting representative conformational ensembles from such datasets is not an easy task and many methods have been developed to tackle it. Our new approach, EnGens (short for ensemble generation), collects these methods into a unified framework for generating and analyzing representative protein conformational ensembles. In this work, we: (1) provide an overview of existing methods and tools for representative protein structural ensemble generation and analysis; (2) unify existing approaches in an open-source Python package, and a portable Docker image, providing interactive visualizations within a Jupyter Notebook pipeline; (3) test our pipeline on a few canonical examples from the literature. Representative ensembles produced by EnGens can be used for many downstream tasks such as protein-ligand ensemble docking, Markov state modeling of protein dynamics and analysis of the effect of single-point mutations.

EnGens: a computational framework for generation and analysis of representative protein conformational ensembles

Proteins are dynamic macromolecules that perform vital functions in cells. A protein structure determines its function, but this structure is not static, as proteins change their conformation to achieve various functions. Understanding the conformational landscapes of proteins is essential to understand their mechanism of action. Sets of carefully chosen conformations can summarize such complex landscapes and provide better insights into protein function than single conformations. We refer to these sets as representative conformational ensembles. Recent advances in computational methods have led to an increase in number of available structural datasets spanning conformational landscapes. However, extracting representative conformational ensembles from such datasets is not an easy task and many methods have been developed to tackle it. Our new approach, EnGens (short for ensemble generation), collects these methods into a unified framework for generating and analyzing protein conformational ensembles. In this work we: (1) provide an overview of existing methods and tools for protein structural ensemble generation and analysis; (2) unify existing approaches in an open-source Python package, and a portable Docker image, providing interactive visualizations within a Jupyter Notebook pipeline; (3) test our pipeline on a few canonical examples found in the literature. Representative ensembles produced by EnGens can be used for many downstream tasks such as protein-ligand ensemble docking, Markov state modeling of protein dynamics and analysis of the effect of single-point mutations.

Structures of the SARS-CoV-2 spike glycoprotein and applications for novel drug development

COVID-19 caused by SARS-CoV-2 has raised a health crisis worldwide. The high morbidity and mortality associated with COVID-19 and the lack of effective drugs or vaccines for SARS-CoV-2 emphasize the urgent need for standard treatment and prophylaxis of COVID-19. The receptor-binding domain (RBD) of the glycosylated spike protein (S protein) is capable of binding to human angiotensin-converting enzyme 2 (hACE2) and initiating membrane fusion and virus entry. Hence, it is rational to inhibit the RBD activity of the S protein by blocking the RBD interaction with hACE2, which makes the glycosylated S protein a potential target for designing and developing antiviral agents. In this study, the molecular features of the S protein of SARS-CoV-2 are highlighted, such as the structures, functions, and interactions of the S protein and ACE2. Additionally, computational tools developed for the treatment of COVID-19 are provided, for example, algorithms, databases, and relevant programs. Finally, recent advances in the novel development of antivirals against the S protein are summarized, including screening of natural products, drug repurposing and rational design. This study is expected to provide novel insights for the efficient discovery of promising drug candidates against the S protein and contribute to the development of broad-spectrum anti-coronavirus drugs to fight against SARS-CoV-2.