3D pharmacophore models are three‐dimensional ensembles of chemically defined interactions of a ligand in its bioactive conformation. They represent an elegant way to decipher chemically encoded ligand information and have therefore become a valuable tool in drug design. In this review, we provide an overview on the basic concept of this method and summarize key studies for applying 3D pharmacophore models in virtual screening and mechanistic studies for protein functionality. Moreover, we discuss recent developments in the field. The combination of 3D pharmacophore models with molecular dynamics simulations could be a quantum leap forward since these approaches consider macromolecule–ligand interactions as dynamic and therefore show a physiologically relevant interaction pattern. Other trends include the efficient usage of 3D pharmacophore information in machine learning and artificial intelligence applications or freely accessible web servers for 3D pharmacophore modeling. The recent developments show that 3D pharmacophore modeling is a vibrant field with various applications in drug discovery and beyond. This article is categorized under: Computer and Information Science > Chemoinformatics Computer and Information Science > Computer Algorithms and Programming Molecular and Statistical Mechanics > Molecular Interactions

Ligands entering a protein binding pocket essentially compete with water molecules for binding to the protein. Hence, the location and thermodynamic properties of water molecules in protein structures have gained increased attention in the drug design community.Including corresponding data into 3D pharmacophore modeling is essential for efficient high throughput virtual screening. Here, we present PyRod, a free and open-source python software that allows for visualization of pharmacophoric binding pocket characteristics, identification of hot spots for ligand binding and subsequent generation of pharmacophore features for virtual screening. The implemented routines analyze the protein environment of water molecules in molecular dynamics (MD) simulations and can differentiate between hydrogen bonded waters as well as waters in a protein environment of hydrophobic, charged or aromatic atom groups. The gathered information is further processed to generate dynamic molecular interaction fields 2 (dMIFs) for visualization and pharmacophoric features for virtual screening. The described software was applied to 5 therapeutically relevant drug targets and generated pharmacophores were evaluated using DUD-E benchmarking sets. The best performing pharmacophore was found for the HIV1 protease with an early enrichment factor of 54.6. PyRod adds a new perspective to structure-based screening campaigns by providing easy-to-interpret dMIFs and purely protein-based pharmacophores that are solely based on tracing water molecules in MD simulations. Since structural information about co-crystallized ligands is not needed, screening campaigns can be followed, for which less or no ligand information is available. PyRod is freely available at https://github.com/schallerdavid/pyrod.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) emerged in late 2019 and since evolved into a global threat with nearly 4.4 million infected people and over 290,000 confirmed deaths worldwide. 1 SARS-CoV-2 is an enveloped virus presenting spike (S) glycoproteins on its outer surface. Binding of S to host cell angiotensin converting enzyme 2 (ACE2) is thought to be critical for cellular entry. The host range of the virus extends far beyond humans and non-human primates. Natural and experimental infections have confirmed high susceptibility of cats, ferrets, and hamsters, whereas dogs, mice, rats, pigs, and chickens seem refractory to SARS-CoV-2 infection. To investigate the reason for the variable susceptibility observed in different species, we have developed molecular descriptors to efficiently analyze our dynamic simulation models of complexes between SARS-CoV-2 S and ACE2. Based on our analyses we predict that: (i) the red squirrel is likely susceptible to SARS-CoV-2 infection and (ii) specific mutations in ACE2 of dogs, rats, and mice render them susceptible to SARS-CoV-2 infection.

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The generation of bioactive molecules from inactive precursors is ac rucial step in the chemical evolution of life, however,mechanistic insights into this aspect of abiogenesis are scarce.Here,weinvestigate the protein-catalyzed formation of antivirals by the 3C-protease of enterovirusD 68. The enzyme induces aldol condensations yielding inhibitors with antiviral activity in cells.K inetic and thermodynamic analyses reveal that the bioactivity emerges from ad ynamic reaction system including inhibitor formation, alkylation of the protein target by the inhibitors,a nd competitive addition of non-protein nucleophiles to the inhibitors.T he most active antivirals are slowlyr eversible inhibitors with elongated target residence times.T he study reveals first examples for the chemical evolution of bio-actives through protein-catalyzed, non-enzymatic CÀCcouplings.The discoveredmechanism works under physiological conditions and might constitute anative process of drug development.