The antipsychotic drug lurasidone inhibits coronaviruses by affecting multiple targets.

Coronaviruses (CoVs) share key genomic elements critical for viral replication, suggesting the feasibility of developing therapeutics with efficacy across different viruses. In a previous work, we demonstrated the antiviral activity of the antipsychotic drug lurasidone against both SARS-CoV-2 and HCoV-OC43. In this study, our investigations on the mechanism of action of lurasidone suggested that the drug exhibits antiviral activity by targeting the papain-like protease (PL-Pro) of both viruses, and the Spike protein of SARS-CoV-2, thereby hampering both the entry and the viral replication.

mdCATH: A Large-Scale MD Dataset for Data-Driven Computational Biophysics.

Recent advancements in protein structure determination are revolutionizing our understanding of proteins. Still, a significant gap remains in the availability of comprehensive datasets that focus on the dynamics of proteins, which are crucial for understanding protein function, folding, and interactions. To address this critical gap, we introduce mdCATH, a dataset generated through an extensive set of all-atom molecular dynamics simulations of a diverse and representative collection of protein domains.

Self-organizing maps of unbiased ligand-target binding pathways and kinetics.

The interpretation of ligand-target interactions at atomistic resolution is central to most efforts in computational drug discovery and optimization. However, the highly dynamic nature of protein targets, as well as possible induced fit effects, makes difficult to sample many interactions effectively with docking studies or even with large-scale molecular dynamics (MD) simulations. We propose a novel application of Self-Organizing Maps (SOMs) to address the sampling and dynamic mapping tasks, particularly in cases involving ligand flexibility and induced fit.

AlloViz: A tool for the calculation and visualisation of protein allosteric communication networks.

Allostery, the presence of functional interactions between distant parts of proteins, is a critical concept in the field of biochemistry and molecular biology, particularly in the context of protein function and regulation. Understanding the principles of allosteric regulation is essential for advancing our knowledge of biology and developing new therapeutic strategies. This paper presents AlloViz, an open-source Python package designed to quantitatively determine, analyse, and visually represent allosteric communication networks on the basis of molecular dynamics (MD) simulation data.