IP6 and PF74 affect HIV-1 Capsid Stability through Modulation of Hexamer-Hexamer Tilt Angle Preference.

The HIV-1 capsid is an irregularly shaped complex of about 1200 protein chains containing the viral genome and several viral proteins. Together, these components are the key to unlocking passage into the nucleus, allowing for permanent integration of the viral genome into the host cell genome. Recent interest into the role of the capsid in viral replication has been driven by the approval of the first-in-class drug lenacapavir, which marks the first drug approved to target a non-enzymatic HIV-1 viral protein. In addition to lenacapavir, other small molecules such as the drug-like compound PF74, and the anionic sugar inositolhexakisphosphate (IP6), are known to impact capsid stability, and although this is widely accepted as a therapeutic effect, the mechanisms through which they do so remain unknown. In this study, we employed a systematic atomistic simulation approach to study the impact of molecules bound to hexamers at the central pore (IP6) and the FG-binding site (PF74) on capsid oligomer dynamics, compared to apo hexamers and pentamers. We found that neither small molecule had a sizeable impact on the free energy of binding of the interface between neighboring hexamers but that both had impacts on the free energy profiles of performing angular deformations to the pair of oligomers akin to the variations in curvature along the irregular surface of the capsid. The IP6 cofactor, on one hand, stabilizes a pair of neighboring hexamers in their flattest configurations, whereas without IP6, the hexamers prefer a high tilt angle between them. On the other hand, having PF74 bound introduces a strong preference for intermediate tilt angles. These results suggest that structural instability is a natural feature of the HIV-1 capsid which is modulated by molecules bound in either the central pore or the FG-binding site. Such modulators, despite sharing many of the same effects on non-bonded interactions at the various protein-protein interfaces, have decidedly different effects on the flexibility of the complex. This study provides a detailed model of the HIV-1 capsid and its interactions with small molecules, informing structure-based drug design, as well as experimental design and interpretation.

Reactive Docking: A Computational Method for High-Throughput Virtual Screenings of Reactive Species.

We describe the formalization of the reactive docking protocol, a method developed to model and predict reactions between small molecules and biological macromolecules. The method has been successfully used in a number of applications already, including recapitulating large proteomics data sets, performing structure-reactivity target optimizations, and prospective virtual screenings. By modeling a near-attack conformation-like state, no QM calculations are required to model the ligand and receptor geometries. Here, we present its generalization using a large data set containing more than 400 ligand-target complexes, 8 nucleophilic modifiable residue types, and more than 30 warheads. The method correctly predicts the modified residue in ∼85% of complexes and shows enrichments comparable to standard focused virtual screenings in ranking ligands. This performance supports this approach for the docking and screening of reactive ligands in virtual chemoproteomics and drug design campaigns.

Ringtail: A Python Tool for Efficient Management and Storage of Virtual Screening Results.

Virtual screening using molecular docking is now routinely used for the rapid evaluation of very large ligand libraries in early stage drug discovery. As the size of compound libraries which can feasibly be screened grows, so do the challenges in result management and storage. Here we introduce Ringtail, a new Python tool in the AutoDock Suite for efficient storage and analysis of virtual screening data based on portable SQLite databases. Ringtail is designed to work with AutoDock-GPU and AutoDock Vina out-of-the-box. Its modular design also allows for easy extension to support input file types from other docking software, different storage solutions, and incorporation into other applications. Ringtail's SQLite database output can dramatically reduce the required disk storage (36-46 fold) by selecting individual poses to store and by taking advantage of the relational database format. Filtering times are also dramatically reduced, requiring minutes to filter millions of ligands. Thus, Ringtail is a tool that can immediately integrate into existing virtual screening pipelines using AutoDock-GPU and Vina, and is scriptable and modifiable to fit specific user needs.

Performance evaluation of flexible macrocycle docking in AutoDock.

Macrocycles represent an important class of ligands, both in natural products and designed drugs. In drug design, macrocyclizations can impart specific ligand conformations and contribute to passive permeation by encouraging intramolecular H-bonds. AutoDock-GPU and Vina can model macrocyclic ligands flexibly, without requiring the enumeration of macrocyclic conformers before docking. Here, we characterize the performance of the method for handling macrocyclic compounds, which is implemented and the default behaviour for ligand preparation with our ligand preparation pipeline, Meeko. A pseudoatom is used to encode bond geometry and produce an anisotropic closure force for macrocyclic rings. This method is evaluated on a diverse set of small molecule and peptide macrocycles, ranging from 7- to 33-membered rings, showing little accuracy loss compared to rigid redocking of the X-ray macrocycle conformers. This suggests that for conformationally flexible macrocycles with unknown binding modes, this method can be effectively used to predict the macrocycle conformation.

Benchmarking the Performance of Irregular Computations in AutoDock-GPU Molecular Docking.

Irregular applications can be found in different scientific fields. In computer-aided drug design, molecular docking simulations play an important role in finding promising drug candidates. AutoDock is a software application widely used for predicting molecular interactions at close distances. It is characterized by irregular computations and long execution runtimes. In recent years, a hardware-accelerated version of AutoDock, called AutoDock-GPU, has been under active development. This work benchmarks the recent code and algorithmic enhancements incorporated into AutoDock-GPU. Particularly, we analyze the impact on execution runtime of techniques based on early termination. These enable AutoDock-GPU to explore the molecular space as necessary, while safely avoiding redundant computations. Our results indicate that it is possible to achieve average runtime reductions of 50% by using these techniques. Furthermore, a comprehensive literature review is also provided, where our work is compared to relevant approaches leveraging hardware acceleration for molecular docking.

COVID 19 repercussions in ophthalmology: a narrative review.

BACKGROUND: The new coronavirus of 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread globally and has repercussions within ophthalmological care. It has caused ocular manifestations in some patients, which can spread through eye secretions. OBJECTIVES: The purpose of this review was to summarize the currently available evidence on COVID-19 with regard to its implications for ophthalmology. DESIGN AND SETTING: Narrative review developed by a research group at Universidade Federal de São Paulo (UNIFESP), São Paulo (SP), Brazil, and at Ludwig-Maximilians-Universität, Munich, Germany. METHODS: We searched the literature on the repercussions of COVID-19 within ophthalmological care, using the MEDLINE and LILACS databases, with the keywords "COVID-19", "ophthalmology" and "coronavirus", from January 1, 2020, to March 27, 2021. Clinical trials, meta-analysis, randomized controlled trials, reviews and systematic reviews were identified. RESULTS: We retrieved 884 references, of which 42 were considered eligible for intensive review and critical analysis. Most of the studies selected reported the evidence regarding COVID-19 and its implications for ophthalmology. CONCLUSIONS: Knowledge of eye symptoms and ocular transmission of the virus remains incomplete. New clinical trials with larger numbers of patients may answer these questions in the future. Moreover, positively, implementation of innovative changes in medicine such as telemedicine and artificial intelligence may assist in diagnosing eye diseases and in training and education for students.

AutoDock Vina 1.2.0: New Docking Methods, Expanded Force Field, and Python Bindings.

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 https://github.com/ccsb-scripps/AutoDock-Vina.

Discovery of Potent Coumarin-Based Kinetic Stabilizers of Amyloidogenic Immunoglobulin Light Chains Using Structure-Based Design.

In immunoglobulin light-chain (LC) amyloidosis, transient unfolding or unfolding and proteolysis enable aggregation of LC proteins, causing potentially fatal organ damage. A drug that kinetically stabilizes LCs could suppress aggregation; however, LC sequences are variable and have no natural ligands, hindering drug development efforts. We previously identified high-throughput screening hits that bind to a site at the interface between the two variable domains of the LC homodimer. We hypothesized that extending the stabilizers beyond this initially characterized binding site would improve affinity. Here, using protease sensitivity assays, we identified stabilizers that can be divided into four substructures. Some stabilizers exhibit nanomolar EC50 values, a 3000-fold enhancement over the screening hits. Crystal structures reveal a key π-π stacking interaction with a conserved tyrosine residue that was not utilized by the screening hits. These data provide a foundation for developing LC stabilizers with improved binding selectivity and enhanced physicochemical properties.

COVID 19 repercussions in ophthalmology: a narrative review

BACKGROUND: The new coronavirus of 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread globally and has repercussions within ophthalmological care. It has caused ocular manifestations in some patients, which can spread through eye secretions. OBJECTIVES: The purpose of this review was to summarize the currently available evidence on COVID-19 with regard to its implications for ophthalmology. DESIGN AND SETTING: Narrative review developed by a research group at Universidade Federal de São Paulo (UNIFESP), São Paulo (SP), Brazil, and at Ludwig-Maximilians-Universität, Munich, Germany. METHODS: We searched the literature on the repercussions of COVID-19 within ophthalmological care, using the MEDLINE and LILACS databases, with the keywords "COVID-19", "ophthalmology" and "coronavirus", from January 1, 2020, to March 27, 2021. Clinical trials, meta-analysis, randomized controlled trials, reviews and systematic reviews were identified. RESULTS: We retrieved 884 references, of which 42 were considered eligible for intensive review and critical analysis. Most of the studies selected reported the evidence regarding COVID-19 and its implications for ophthalmology. CONCLUSIONS: Knowledge of eye symptoms and ocular transmission of the virus remains incomplete. New clinical trials with larger numbers of patients may answer these questions in the future. Moreover, positively, implementation of innovative changes in medicine such as telemedicine and artificial intelligence may assist in diagnosing eye diseases and in training and education for students.

Accelerating AutoDock4 with GPUs and Gradient-Based Local Search.

AutoDock4 is a widely used program for docking small molecules to macromolecular targets. It describes ligand-receptor interactions using a physics-inspired 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.

A Strain-Specific Inhibitor of Receptor-Bound HIV-1 Targets a Pocket near the Fusion Peptide.

Disruption of viral fusion represents a viable, albeit under-explored, target for HIV therapeutics. Here, while studying the receptor-bound envelope glycoprotein conformation by cryoelectron microscopy (cryo-EM), we identify a pocket near the base of the trimer containing a bound detergent molecule and perform in silico drug screening by using a library of drug-like and commercially available molecules. After down-selection, we solve cryo-EM structures that validate the binding of two small molecule hits in very similar manners to the predicted binding poses, including interactions with aromatic residues within the fusion peptide. One of the molecules demonstrates low micromolar inhibition of the autologous virus by using a very rare phenylalanine in the fusion peptide and stabilizing the surrounding region. This work demonstrates that small molecules can target the fusion process, providing an additional target for anti-HIV therapeutics, and highlights the need to explore how fusion peptide sequence variations affect receptor-mediated conformational states across diverse HIV strains.

Initial Analysis of the Arylomycin D Antibiotics.

The arylomycins are a class of natural product antibiotics that inhibit bacterial type I signal peptidase and are under development as therapeutics. Four classes of arylomycins are known, arylomycins A-D. Previously, we reported the synthesis and analysis of representatives of the A, B, and C classes and showed that their spectrum of activity has the potential to be much broader than originally assumed. Along with a comparison of the mechanism of acquired and innate resistance, this led us to suggest that the arylomycins are latent antibiotics, antibiotics that once possessed broad-spectrum activity, but which upon examination today, have only narrow spectrum activity due to prior selection for resistance in the course of the competition with other microorganisms that drove their evolution in the first place. Interestingly, actinocarbasin, the only identified member of the arylomycin D class, has been reported to have activity against MRSA. To confirm and understand this activity, several actinocarbasin derivatives were synthesized. We demonstrate that the previously reported structure of actinocarbasin is incorrect, identify what is likely the correct scaffold, confirm that scaffold has activity against MRSA, and determine the origin of this activity.

Structural basis for the stabilization of amyloidogenic immunoglobulin light chains by hydantoins.

Misfolding and aggregation of immunoglobulin light chains (LCs) leads to the degeneration of post-mitotic tissue in the disease immunoglobulin LC amyloidosis (AL). We previously reported the discovery of small molecule kinetic stabilizers of the native dimeric structure of full-length LCs, which slow or stop the LC aggregation cascade at the outset. A predominant structural category of kinetic stabilizers emerging from the high-throughput screen are coumarins substituted at the 7-position, which bind at the interface between the two variable domains of the light chain dimer. Here, we report the binding mode of another, more polar, LC kinetic stabilizer chemotype, 3,5-substituted hydantoins. Computational docking, solution nuclear magnetic resonance experiments, and x-ray crystallography show that the aromatic substructure emerging from the hydantoin 3-position occupies the same LC binding site as the coumarin ring. Notably, the hydantoin ring extends beyond the binding site mapped out by the coumarin hits. The hydantoin ring makes hydrogen bonds with both LC monomers simultaneously. The alkyl substructure at the hydantoin 5-position partially occupies a novel binding pocket proximal to the pocket occupied by the coumarin substructure. Overall, the hydantoin structural data suggest that a larger area of the LC variable-domain-variable-domain dimer interface is amenable to small molecule binding than previously demonstrated, which should facilitate development of more potent full-length LC kinetic stabilizers.

GPU-Accelerated Drug Discovery with Docking on the Summit Supercomputer: Porting, Optimization, and Application to COVID-19 Research.

Protein-ligand docking is an in silico tool used to screen potential drug compounds for their ability to bind to a given protein receptor within a drug-discovery campaign. Experimental drug screening is expensive and time consuming, and it is desirable to carry out large scale docking calculations in a high-throughput manner to narrow the experimental search space. Few of the existing computational docking tools were designed with high performance computing in mind. Therefore, optimizations to maximize use of high-performance computational resources available at leadership-class computing facilities enables these facilities to be leveraged for drug discovery. Here we present the porting, optimization, and validation of the AutoDock-GPU program for the Summit supercomputer, and its application to initial compound screening efforts to target proteins of the SARS-CoV-2 virus responsible for the current COVID-19 pandemic.

Charting Hydrogen Bond Anisotropy.

A hydrogen bond (HB) is an essential interaction in countless phenomena, regulating the chemistry of life. HBs are characterized by two features, strength and directionality, with a high degree of heterogeneity across different chemical groups. These characteristics are dependent on the electronic configuration of the atoms involved in the interaction, which, in turn, is influenced strongly by the local molecular environment. Studies based on the analysis of HB in the solid phase, such as X-ray crystallography, suffer from significant biases due to packing forces. These will tend to better describe strong HBs at the expenses of weak ones, which will be either distorted or under-represented. Using quantum mechanics (QM), we calculated interaction energies for about a hundred acceptors and donors in a rigorously defined set of geometries. We performed 180,000 independent QM calculations, covering all relevant angular components, mapping strength and directionality in a context free from external biases, with both single-site and cooperative HBs. By quantifying directionality, we show that there is no correlation with strength; therefore, these two components need to be addressed separately. Results demonstrate that there are very strong HB acceptors (e.g., dimethyl sulfoxide) with nearly isotropic interactions and weak ones (e.g., thioacetone) with a sharp directional profile. Similarly, groups can have comparable directional propensity but be very distant in the strength spectrum (e.g., thioacetone and pyridine). Results provide a new perspective on the way HB directionality is described, with implications for biophysics and molecular recognition that ultimately can influence chemical biology, protein engineering, and drug design.

Structural basis for strand-transfer inhibitor binding to HIV intasomes.

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.

D3R Grand Challenge 4: prospective pose prediction of BACE1 ligands with AutoDock-GPU.

In this paper we describe our approaches to predict the binding mode of twenty BACE1 ligands as part of Grand Challenge 4 (GC4), organized by the Drug Design Data Resource. Calculations for all submissions (except for one, which used AutoDock4.2) were performed using AutoDock-GPU, the new GPU-accelerated version of AutoDock4 implemented in OpenCL, which features a gradient-based local search. The pose prediction challenge was organized in two stages. In Stage 1a, the protein conformations associated with each of the ligands were undisclosed, so we docked each ligand to a set of eleven receptor conformations, chosen to maximize the diversity of binding pocket topography. Protein conformations were made available in Stage 1b, making it a re-docking task. For all calculations, macrocyclic conformations were sampled on the fly during docking, taking the target structure into account. To leverage information from existing structures containing BACE1 bound to ligands available in the PDB, we tested biased docking and pose filter protocols to facilitate poses resembling those experimentally determined. Both pose filters and biased docking resulted in more accurate docked poses, enabling us to predict for both Stages 1a and 1b ligand poses within 2 Å RMSD from the crystallographic pose. Nevertheless, many of the ligands could be correctly docked without using existing structural information, demonstrating the usefulness of physics-based scoring functions, such as the one used in AutoDock4, for structure based drug design.

Comparison of affinity ranking using AutoDock-GPU and MM-GBSA scores for BACE-1 inhibitors in the D3R Grand Challenge 4.

Molecular docking has been successfully used in computer-aided molecular design projects for the identification of ligand poses within protein binding sites. However, relying on docking scores to rank different ligands with respect to their experimental affinities might not be sufficient. It is believed that the binding scores calculated using molecular mechanics combined with the Poisson-Boltzman surface area (MM-PBSA) or generalized Born surface area (MM-GBSA) can predict binding affinities more accurately. In this perspective, we decided to take part in Stage 2 of the Drug Design Data Resource (D3R) Grand Challenge 4 (GC4) to compare the performance of a quick scoring function, AutoDock4, to that of MM-GBSA in predicting the binding affinities of a set of [Formula: see text]-Amyloid Cleaving Enzyme 1 (BACE-1) ligands. Our results show that re-scoring docking poses using MM-GBSA did not improve the correlation with experimental affinities. We further did a retrospective analysis of the results and found that our MM-GBSA protocol is sensitive to details in the protein-ligand system: (i) neutral ligands are more adapted to MM-GBSA calculations than charged ligands, (ii) predicted binding affinities depend on the initial conformation of the BACE-1 receptor, (iii) protonating the aspartyl dyad of BACE-1 correctly results in more accurate binding affinity predictions.

SuFEx-enabled, agnostic discovery of covalent inhibitors of human neutrophil elastase.

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.

Mechanistic Pathway on Human α-Glucosidase Maltase-Glucoamylase Unveiled by QM/MM Calculations.

The excessive consumption of starch in human diets is associated with highly prevalent chronic metabolic diseases such as type 2 diabetes and obesity. α-Glucosidase enzymes contribute to the digestion of starch into glucose and are thus attractive therapeutic targets for diabetes. Given that the active sites of the various families of α-glucosidases have different sizes and structural features, atomistic descriptions of the catalytic mechanisms of these enzymes can support the development of potent and selective new inhibitors. Maltase-glucoamylase (MGAM), in particular, has a N-terminal catalytic domain (NtMGAM) that has shown high inhibitor selectivity. We provide here the first theoretical study of the human NtMGAM catalytic domain, employing a hybrid QM/MM approach with the ONIOM method to disclose the full atomistic details of the reactions promoted by this domain. We observed that the catalytic activity follows the classical Koshland double-displacement mechanistic pathway that uses general acid and base catalysts. A covalent glycosyl-enzyme intermediate was formed and hydrolyzed in the first and second mechanistic steps, respectively, through oxocarbenium ion-like transition state structures. The overall reaction is of dissociative type. Both transition state geometries differ from those known to occur in other glycosidases. The activation free energy for the glycosylation rate-limiting step agrees with the experimental barrier of 15.8 kcal·mol-1. Both individual mechanistic steps of the reaction are exoergonic. These structural results may serve as the basis for the design of transition state analogue inhibitors that specifically target the intestinal NtMGAM catalytic domain, thus delaying the production of glucose in diabetic and obese patients.