AutoDock Vina is arguably one of the fastest and most widely used open-source programs for molecular docking. However, compared to other programs in the AutoDock Suite, it lacks support for modeling specific features such as macrocycles or explicit water molecules. Here, we describe the implementation of this functionality in AutoDock Vina 1.2.0. Additionally, AutoDock Vina 1.2.0 supports the AutoDock4.2 scoring function, simultaneous docking of multiple ligands, and a batch mode for docking a large number of ligands. Furthermore, we implemented Python bindings to facilitate scripting and the development of docking workflows. This work is an effort toward the unification of the features of the AutoDock4 and AutoDock Vina programs. The source code is available at .

Zinc is present in a wide variety of proteins and is important in the metabolism of most organisms. Zinc metalloenzymes are therapeutically relevant targets in diseases such as cancer, heart disease, bacterial infection, and Alzheimer’s disease. In most cases a drug molecule targeting such enzymes establishes an interaction that coordinates with the zinc ion. Thus, accurate prediction of the interaction of ligands with zinc is an important aspect of computational docking and virtual screening against zinc containing proteins. We have extended the AutoDock force field to include a specialized potential describing the interactions of zinc-coordinating ligands. This potential describes both the energetic and geometric components of the interaction. The new force field, named AutoDock4Zn, was calibrated on a data set of 292 crystal complexes containing zinc. Redocking experiments show that the force field provides significant improvement in performance in both free energy of binding estimation as well as in root-mean-square deviation from the crystal structure pose. The new force field has been implemented in AutoDock without modification to the source code.

Cholesterol is an essential component of cell membranes and the precursor for the synthesis of steroid hormones and bile acids. The synthesis of this molecule occurs partially in a membranous world (especially the last steps), where the enzymes, substrates, and products involved tend to be extremely hydrophobic. The importance of cholesterol has increased in the past half-century because of its association with cardiovascular diseases, which are considered one of the leading causes of death worldwide. In light of the current need for new drugs capable of controlling the levels of cholesterol in the bloodstream, it is important to understand how cholesterol is synthesized in the organism and identify the main enzymes involved in this process. Taking this into account, this review presents a detailed description of several enzymes involved in the biosynthesis of cholesterol. In this regard, the structure and catalytic mechanism of the enzymes involved in cholesterol biosynthesis, from the initial two-carbon acetyl-CoA building block, will be reviewed and their current pharmacological importance discussed. We believe that this review may contribute to a deeper level of understanding of cholesterol metabolism and that it will serve as a useful resource for future studies of the cholesterol biosynthesis pathway.

The HIV intasome is a large nucleoprotein assembly that mediates the integration of a DNA copy of the viral genome into host chromatin. Intasomes are targeted by the latest generation of antiretroviral drugs, integrase strand-transfer inhibitors (INSTIs). Challenges associated with lentiviral intasome biochemistry have hindered high-resolution structural studies of how INSTIs bind to their native drug target. Here, we present high-resolution cryo–electron microscopy structures of HIV intasomes bound to the latest generation of INSTIs. These structures highlight how small changes in the integrase active site can have notable implications for drug binding and design and provide mechanistic insights into why a leading INSTI retains efficacy against a broad spectrum of drug-resistant variants. The data have implications for expanding effective treatments available for HIV-infected individuals.

AUTODOCK4 is a widely used program for docking small molecules to macromolecular targets. It describes ligand−receptor interactions using a physicsinspired scoring function that has been proven useful in a variety of drug discovery projects. However, compared to more modern and recent software, AUTODOCK4 has longer execution times, limiting its applicability to large scale dockings. To address this problem, we describe an OpenCL implementation of AUTODOCK4, called AUTODOCK-GPU, that leverages the highly parallel architecture of GPU hardware to reduce docking runtime by up to 350-fold with respect to a single-threaded process. Moreover, we introduce the gradient-based local search method ADADELTA, as well as an improved version of the Solis-Wets random optimizer from AUTODOCK4. These efficient local search algorithms significantly reduce the number of calls to the scoring function that are needed to produce good results. The improvements reported here, both in terms of docking throughput and search efficiency, facilitate the use of the AUTODOCK4 scoring function in large scale virtual screening.

<pre>AutoDock Vina is arguably one of the fastest and most widely used open-source docking engines. However, compared to other docking engines in the AutoDock Suite, it lacks features that support modeling of specific systems such as macrocycles or modeling water explicitly. Here, we describe the implementation of these functionality in AutoDock Vina 1.2.0. Additionally, AutoDock Vina 1.2.0 supports the AutoDock4.2 scoring function, simultaneous docking of multiple ligands, and a batch mode for docking a large number of ligands. Furthermore, we implemented Python bindings to facilitate scripting and the development of docking workflows. This work is an effort toward the unification of the features of the AutoDock4 and AutoDock Vina docking engines. The source code is available at <a href="https://github.com/ccsb-scripps/AutoDock-Vina" rel="noopener noreferrer" target="_blank">https://github.com/ccsb-scripps/AutoDock-Vina</a></pre>

We present a supercomputer-driven pipeline for in silico drug discovery using enhanced sampling molecular dynamics (MD) and ensemble docking. Ensemble docking makes use of MD results by docking compound databases into representative protein binding-site conformations, thus taking into account the dynamic properties of the binding sites. We also describe preliminary results obtained for 24 systems involving eight proteins of the proteome of SARS-CoV-2. The MD involves temperature replica exchange enhanced sampling, making use of massively parallel supercomputing to quickly sample the configurational space of protein drug targets. Using the Summit supercomputer at the Oak Ridge National Laboratory, more than 1 ms of enhanced sampling MD can be generated per day. We have ensemble docked repurposing databases to 10 configurations of each of the 24 SARS-CoV-2 systems using AutoDock Vina. Comparison to experiment demonstrates remarkably high hit rates for the top scoring tranches of compounds identified by our ensemble approach. We also demonstrate that, using Autodock-GPU on Summit, it is possible to perform exhaustive docking of one billion compounds in under 24 h. Finally, we discuss preliminary results and planned improvements to the pipeline, including the use of quantum mechanical (QM), machine learning, and artificial intelligence (AI) methods to cluster MD trajectories and rescore docking poses.

Sulfur fluoride exchange (SuFEx) has emerged as the new generation of click chemistry. We report here a SuFEx-enabled, agnostic approach for the discovery and optimization of covalent inhibitors of human neutrophil elastase (hNE). Evaluation of our ever-growing collection of SuFExable compounds toward various biological assays unexpectedly revealed a selective and covalent hNE inhibitor: benzene-1,2-disulfonyl fluoride. Synthetic derivatization of the initial hit led to a more potent agent, 2-(fluorosulfonyl)phenyl fluorosulfate with IC50 0.24 μM and greater than 833-fold selectivity over the homologous neutrophil serine protease, cathepsin G. The optimized, yet simple benzenoid probe only modified active hNE and not its denatured form.