Accelerated CDOCKER with GPUs, Parallel Simulated Annealing, and Fast Fourier Transforms

Development of novel ALOX15 inhibitors combining dual machine learning filtering and fragment substitution optimisation approaches, molecular docking and dynamic simulation methods

The oxidation of unsaturated lipids, facilitated by the enzyme Arachidonic acid 15-lipoxygenase (ALOX15), is an essential element in the development of ferroptosis. This study combined a dual-score exclusion strategy with high-throughput virtual screening, naive Bayesian and recursive partitioning machine learning models, the already established ALOX15 inhibitor i472, and a docking-based fragment substitution optimisation approach to identify potential ALOX15 inhibitors, ultimately leading to the discovery of three FDA-approved drugs that demonstrate optimal inhibitory potential against ALOX15. Through fragment substitution-based optimisation, seven new inhibitor structures have been developed. To evaluate their practicality, ADMET predictions and molecular dynamics simulations were performed. In conclusion, the compounds found in this study provide a novel approach to combat conditions related to ferroptosis-related injury by inhibiting ALOX15.

Guiding discovery of protein sequence-structure-function modeling

MOTIVATION

Protein engineering techniques are key in designing novel catalysts for a wide range of reactions. Although approaches vary in their exploration of the sequence-structure-function paradigm, they are often hampered by the labor-intensive steps of protein expression and screening. In this work, we describe the development and testing of a high-throughput in silico sequence-structure-function pipeline using AlphaFold2 and fast Fourier transform docking that is benchmarked with enantioselectivity and reactivity predictions for an ancestral sequence library of fungal flavin-dependent monooxygenases.

RESULTS

The predicted enantioselectivities and reactivities correlate well with previously described screens of an experimentally available subset of these proteins and capture known changes in enantioselectivity across the phylogenetic tree representing ancestorial proteins from this family. With this pipeline established as our functional screen, we apply ensemble decision tree models and explainable AI techniques to build sequence-function models and extract critical residues within the binding site and the second-sphere residues around this site. We demonstrate that the top-identified key residues in the control of enantioselectivity and reactivity correspond to experimentally verified residues. The in silico sequence-to-function pipeline serves as an accelerated framework to inform protein engineering efforts from vast informative sequence landscapes contained in protein families, ancestral resurrects, and directed evolution campaigns.

AVAILABILITY

Jupyter notebooks detailing the sequence-structure-function pipeline are available at https://github.com/BrooksResearchGroup-UM/seq_struct_func.

FASTDock: A Pipeline for Allosteric Drug Discovery

Allostery is involved in innumerable biological processes and plays a fundamental role in human disease. Thus, the exploration of allosteric modulation is crucial for research on biological mechanisms and in the development of novel therapeutics. The development of small-molecule allosteric effectors can be used as tools to probe biological mechanisms of interest. One of the main limitations in targeting allosteric sites is the difficulty in uncovering them for specific receptors. Furthermore, upon discovery of novel allosteric modulation, early lead generation is made more difficult as compared to that at orthosteric sites because there is likely no information about the types of molecules that can bind at the site. In the work described here, we present a novel drug discovery pipeline, FASTDock, which allows one to uncover ligandable sites as well as small molecules that target the given site without requiring pre-existing knowledge of ligands that can bind in the targeted site. By using a hierarchical screening strategy, this method has the potential to enable high-throughput screens of an exceptionally large database of targeted ligand space.

Pharmacophore-based virtual screening of ZINC database, molecular modeling and designing new derivatives as potential HDAC6 inhibitors

To date, many HDAC6 inhibitors have been identified and developed but none is clinically approved as of now. Through this study, we aim to obtain novel HDAC6 selective inhibitors and provide new insights into the detailed structural design of potential HDAC6 inhibitors. A HypoGen-based 3D QSAR HDAC6 pharmacophore was built and used as a query model to screen approximately 8 million ZINC database compounds. First, the ZINC Database was filtered using ADMET, followed by pharmacophore-based library screening. Using fit value and estimated activity cutoffs, a final set of 54 ZINC hits was obtained that were further investigated using molecular docking with the crystal structure of human histone deacetylase 6 catalytic domain 2 in complex with Trichostatin A (PDB ID: 5EDU). Through detailed in silico screening of the ZINC database, we shortlisted three hits as the lead molecules for designing novel HDAC6 inhibitors with better efficacy. Docking with 5EDU, followed by ADMET and TOPKAT analysis of modified ZINC hits provided 9 novel potential HDAC6 inhibitors that possess better docking scores and 2D interactions as compared to the control ZINC hit molecules. Finally, a 50 ns MD analysis run followed by Protein-Ligand Interaction Energy (PLIE) analysis of the top scored hits provided a novel molecule N1 that showed promisingly similar results to that of Ricolinostat (a known HDAC6 inhibitor). The comparable result of the designed hits to established HDAC6 inhibitors suggests that these compounds might prove to be successful HDAC6 inhibitors in future. Designed novel hits that might act as good HDAC6 inhibitors derived from ZINC database using combined molecular docking and modeling approaches.

Recent Developments in Ultralarge and Structure-Based Virtual Screening Approaches

Drug development is a wide scientific field that faces many challenges these days. Among them are extremely high development costs, long development times, and a small number of new drugs that are approved each year. New and innovative technologies are needed to solve these problems that make the drug discovery process of small molecules more time and cost efficient, and that allow previously undruggable receptor classes to be targeted, such as protein-protein interactions. Structure-based virtual screenings (SBVSs) have become a leading contender in this context. In this review, we give an introduction to the foundations of SBVSs and survey their progress in the past few years with a focus on ultralarge virtual screenings (ULVSs). We outline key principles of SBVSs, recent success stories, new screening techniques, available deep learning-based docking methods, and promising future research directions. ULVSs have an enormous potential for the development of new small-molecule drugs and are already starting to transform early-stage drug discovery.

pyCHARMM: Embedding CHARMM Functionality in a Python Framework

CHARMM is rich in methodology and functionality as one of the first programs addressing problems of molecular dynamics and modeling of biological macromolecules and their partners, e.g., small molecule ligands. When combined with the highly developed CHARMM parameters for proteins, nucleic acids, small molecules, lipids, sugars, and other biologically relevant building blocks, and the versatile CHARMM scripting language, CHARMM has been a trendsetting platform for modeling studies of biological macromolecules. To further enhance the utility of accessing and using CHARMM functionality in increasingly complex workflows associated with modeling biological systems, we introduce pyCHARMM, Python bindings, functions, and modules to complement and extend the extensive set of modeling tools and methods already available in CHARMM. These include access to CHARMM function-generated variables associated with the system (psf), coordinates, velocities and forces, atom selection variables, and force field related parameters. The ability to augment CHARMM forces and energies with energy terms or methods derived from machine learning or other sources, written in Python, CUDA, or OpenCL and expressed as Python callable routines is introduced together with analogous functions callable during dynamics calculations. Integration of Python-based graphical engines for visualization of simulation models and results is also accessible. Loosely coupled parallelism is available for workflows such as free energy calculations, using MBAR/TI approaches or high-throughput multisite λ-dynamics (MSλD) free energy methods, string path optimization calculations, replica exchange, and molecular docking with a new Python-based CDOCKER module. CHARMM accelerated platform kernels through the CHARMM/OpenMM API, CHARMM/DOMDEC, and CHARMM/BLaDE API are also readily integrated into this Python framework. We anticipate that pyCHARMM will be a robust platform for the development of comprehensive and complex workflows utilizing Python and its extensive functionality as well as an optimal platform for users to learn molecular modeling methods and practices within a Python-friendly environment such as Jupyter Notebooks.

TMPRSS2 Inhibitor Discovery Facilitated through an In Silico and Biochemical Screening Platform

The COVID-19 pandemic has highlighted the need for new antiviral approaches because many of the currently approved drugs have proven ineffective against mitigating SARS-CoV-2 infections. The host transmembrane serine protease TMPRSS2 is a promising antiviral target because it plays a role in priming the spike protein before viral entry occurs for the most virulent variants. Further, TMPRSS2 has no established physiological role, thereby increasing its attractiveness as a target for antiviral agents. Here, we utilize virtual screening to curate large libraries into a focused collection of potential inhibitors. Optimization of a recombinant expression and purification protocol for the TMPRSS2 peptidase domain facilitates subsequent biochemical screening and characterization of selected compounds from the curated collection in a kinetic assay. In doing so, we identify new noncovalent TMPRSS2 inhibitors that block SARS-CoV-2 infectivity in a cellular model. One such inhibitor, debrisoquine, has high ligand efficiency, and an initial structure-activity relationship study demonstrates that debrisoquine is a tractable hit compound for TMPRSS2.

Deciphering the evolution of flavin-dependent monooxygenase stereoselectivity using ancestral sequence reconstruction

Controlling the selectivity of a reaction is critical for target-oriented synthesis. Accessing complementary selectivity profiles enables divergent synthetic strategies, but is challenging to achieve in biocatalytic reactions given enzymes' innate preferences of a single selectivity. Thus, it is critical to understand the structural features that control selectivity in biocatalytic reactions to achieve tunable selectivity. Here, we investigate the structural features that control the stereoselectivity in an oxidative dearomatization reaction that is key to making azaphilone natural products. Crystal structures of enantiocomplementary biocatalysts guided the development of multiple hypotheses centered on the structural features that control the stereochemical outcome of the reaction; however, in many cases, direct substitutions of active site residues in natural proteins led to inactive enzymes. Ancestral sequence reconstruction (ASR) and resurrection were employed as an alternative strategy to probe the impact of each residue on the stereochemical outcome of the dearomatization reaction. These studies suggest that two mechanisms are active in controlling the stereochemical outcome of the oxidative dearomatization reaction: one involving multiple active site residues in AzaH and the other dominated by a single Phe to Tyr switch in TropB and AfoD. Moreover, this study suggests that the flavin-dependent monooxygenases (FDMOs) adopt simple and flexible strategies to control stereoselectivity, which has led to stereocomplementary azaphilone natural products produced by fungi. This paradigm of combining ASR and resurrection with mutational and computational studies showcases sets of tools for understanding enzyme mechanisms and provides a solid foundation for future protein engineering efforts.

Novel hybrid virtual screening protocol based on pharmacophore and molecular docking for discovery of GSK-3β inhibitors

GSK-3β is a member of the GSKs subfamily and plays a major role in the regulation of transcriptional elongation, which has attracted widespread attention as a therapeutic target for AD. In this study, by combining pharmacophore-based virtual screening and kinase inhibition assays, we have successfully identified four small molecules that inhibit GSK-3β activity at micromolar potency. These hit compounds showed drug-like properties according to Lipinski's rule of five and ADMET. An inter-complex interaction study showed that all hit compounds adapted well to the ATP pocket of the GSK-3β protein. Among them, hits 2 and 4 displayed considerable inhibitory activities with IC₅₀ value of 0.74 ± 0.04 μM and 2.32 ± 0.84 μM respectively. Overall, the discovered GSK-3β inhibitors act as new chemical leads to develop improved inhibitors that block the interaction of GSK-3β, and the hybrid virtual screening strategy designed in this study provides an important reference for design and synthesis novel selective GSK-3β inhibitors.

Structures, biomimetic synthesis, and anti-SARS-CoV-2 activity of two pairs of enantiomeric phenylpropanoid-conjugated protoberberine alkaloids from the rhizomes of Corydalis decumbens

(±)-Decumicorine A (1) and (±)-epi-decumicorine A (2), two pairs of enantiomeric isoquinoline alkaloids featuring a novel phenylpropanoid-conjugated protoberberine skeleton, were isolated and purified from the rhizomes of Corydalis decumbens. The separation of (±)-1 and (±)-2 was achieved by chiral HPLC to produce four optically pure enantiomers. The structures and absolute configurations of compounds (−)-1, (+)-1, (−)-2, and (+)-2 were elucidated by spectroscopic analysis, ECD calculations, and X-ray crystallographic analyses. The two racemates were generated from a Diels-Alder [4 + 2] cycloaddition between jatrorrhizine and ferulic acid in the proposed biosynthetic pathways, which were fully verified by a biomimetic synthesis. Moreover, compound (+)-1 exhibited an antiviral entry effect on SARS-CoV-2 pseudovirus by blocking spike binding to the ACE2 receptor on HEK-293T-ACE2^h host cells.

Covalent docking in CDOCKER

Targeted covalent inhibitors (TCIs) are considered to be an important component in the toolbox of drug discovery and about 30% of currently marketed drugs are TCIs. Although these drugs raise concerns about toxicity, their high potencies and prolonged effects result in less-frequent drug dosing and wide therapeutic margins for patients. This leads to increased interests in developing new computational methods to identify novel covalent inhibitors. The implementation of successful in silico docking algorithms have the potential to provide significant savings of time and money in the discovery of lead compounds. In this paper, we describe the implementation and testing of a covalent docking methodology in Rigid CDOCKER and the optimization of the corresponding physics-based scoring function with an additional customizable covalent bond grid potential which represents the free energy change of bond formation between the ligand and the receptor. We optimize the covalent bond grid potential for different common covalent bond formation reaction in TCIs. The average runtime for docking one covalent compound is 15 minutes which is comparable or faster than other well-established covalent docking methods. We demonstrate comparable top rank accuracy compared with other covalent docking algorithms using the pose prediction benchmark dataset for covalent docking algorithms developed by the Keserű group. Finally, we construct a retrospective virtual screening benchmark dataset containing 8 different receptor targets with different covalent bond formation reactions. To our knowledge, this is the largest dataset for benchmarking covalent docking methods. We show that our new covalent docking algorithm has the ability to identify lead compounds among a large chemical space. The largest AUC value is 0.909 for the target receptor CATK and the warhead chemistry of the covalent inhibitors is addition to the aldehyde functionality.

Structure-guided pharmacophore based virtual screening, docking, and molecular dynamics to discover repurposed drugs as novel inhibitors against endoribonuclease Nsp15 of SARS-CoV-2

COVID-19 (Corona Virus Disease of 2019) caused by the novel 'Severe Acute Respiratory Syndrome Coronavirus-2' (SARS-CoV-2) has wreaked havoc on human health and the global economy. As a result, for new medication development, it's critical to investigate possible therapeutic targets against the novel virus. 'Non-structural protein 15' (Nsp15) endonuclease is one of the crucial targets which helps in the replication of virus and virulence in the host immune system. Here, in the current study, we developed the structure-based pharmacophore model based on Nsp15-UMP interactions and virtually screened several databases against the selected model. To validate the screening process, we docked the top hits obtained after secondary filtering (Lipinski's rule of five, ADMET & Topkat) followed by 100 ns molecular dynamics (MD) simulations. Next, to revalidate the MD simulation studies, we have calculated the binding free energy of each complex using the MM-PBSA procedure. The discovered repurposed drugs can aid the rational design of novel inhibitors for Nsp15 of the SARS-CoV-2 enzyme and may be considered for immediate drug development.

Accelerating AutoDock Vina with GPUs

AutoDock Vina is one of the most popular molecular docking tools. In the latest benchmark CASF-2016 for comparative assessment of scoring functions, AutoDock Vina won the best docking power among all the docking tools. Modern drug discovery is facing a common scenario of large virtual screening of drug hits from huge compound databases. Due to the seriality characteristic of the AutoDock Vina algorithm, there is no successful report on its parallel acceleration with GPUs. Current acceleration of AutoDock Vina typically relies on the stack of computing power as well as the allocation of resource and tasks, such as the VirtualFlow platform. The vast resource expenditure and the high access threshold of users will greatly limit the popularity of AutoDock Vina and the flexibility of its usage in modern drug discovery. In this work, we proposed a new method, Vina-GPU, for accelerating AutoDock Vina with GPUs, which is greatly needed for reducing the investment for large virtual screens and also for wider application in large-scale virtual screening on personal computers, station servers or cloud computing, etc. Our proposed method is based on a modified Monte Carlo using simulating annealing AI algorithm. It greatly raises the number of initial random conformations and reduces the search depth of each thread. Moreover, a classic optimizer named BFGS is adopted to optimize the ligand conformations during the docking progress, before a heterogeneous OpenCL implementation was developed to realize its parallel acceleration leveraging thousands of GPU cores. Large benchmark tests show that Vina-GPU reaches an average of 21-fold and a maximum of 50-fold docking acceleration against the original AutoDock Vina while ensuring their comparable docking accuracy, indicating its potential for pushing the popularization of AutoDock Vina in large virtual screens.

Novel Imidazole Phenoxyacetic Acids as Inhibitors of USP30 for Neuroprotection Implication via the Ubiquitin-Rho-110 Fluorometric Assay: Design, Synthesis, and In Silico and Biochemical Assays

USP30, a deubiquitinating enzyme family, forfeits the ubiquitination of E3 ligase and Parkin on the surface of mitochondria. Inhibition of USP30 results in mitophagy and cellular clearance. Herein, by understanding structural requirements, we discovered potential USP30 inhibitors from an imidazole series of ligands via a validated ubiquitin-rhodamine-110 fluorometric assay. A novel catalytic use of the Zn(l-proline)₂ complex for the synthesis of tetrasubstituted imidazoles was identified. Among all compounds investigated, 3g and 3f inhibited USP30 at IC₅₀ of 5.12 and 8.43 μM, respectively. The binding mode of compounds at the USP30 binding site was understood by a docking study and interactions with the key amino acids were identified. Compound 3g proved its neuroprotective efficacy by inhibiting apoptosis on SH-SY5Y neuroblastoma cells against dynorphin A (10 μM) treatment. Hence, the present study provides a new protocol to design and develop ligands against USP30, thereby offering a therapeutic strategy under conditions like kidney damage and neurodegenerative disorders including Parkinson's disease.

I2-Catalyzed Carbonylation of α-Methylene Ketones to Synthesize 1,2-Diaryl Diketones and Antiviral Quinoxalines in One Pot

An efficient approach for the synthesis of 1,2-diaryl diketones was developed from readily available α-methylene ketones by catalysis of I2. In the same oxidation system, a novel one-pot procedure was established for the construction of antiviral and anticancer quinoxalines. The reactions proceeded well with a wide variety of substrates and good functional group tolerance, affording desired compounds in moderate to excellent yields. Quinoxalines 4ca and 4ad inhibited viral entry of SARS-CoV-2 spike pseudoviruses into HEK-293T-ACE2h host cells as dual blockers of both human ACE2 receptor and viral spike RBD with IC50 values of 19.70 and 21.28 μM, respectively. In addition, cytotoxic evaluation revealed that 4aa, 4ba, 4ia, and 4ab suppressed four cancer cells with IC50 values ranging from 6.25 to 28.55 μM.

Flexible CDOCKER: Hybrid Searching Algorithm and Scoring Function with Side Chain Conformational Entropy

The binding of small-molecule ligands to protein or nucleic acid targets is important to numerous biological processes. Accurate prediction of the binding modes between a ligand and a macromolecule is of fundamental importance in structure-based structure-function exploration. When multiple ligands with different sizes are docked to a target receptor, it is reasonable to assume that the residues in the binding pocket may adopt alternative conformations upon interacting with the different ligands. In addition, it has been suggested that the entropic contribution to binding can be important. However, only a few attempts to include the side chain conformational entropy upon binding within the application of flexible receptor docking methodology exist. Here, we propose a new physics-based scoring function that includes both enthalpic and entropic contributions upon binding by considering the conformational variability of the flexible side chains within the ensemble of docked poses. We also describe a novel hybrid searching algorithm that combines both molecular dynamics (MD)-based simulated annealing and genetic algorithm crossovers to address the enhanced sampling of the increased search space. We demonstrate improved accuracy in flexible cross-docking experiments compared with rigid cross-docking. We test our developments by considering five protein targets, thrombin, dihydrofolate reductase(DHFR), T4 L99A, T4 L99A/M102Q, and PDE10A, which belong to different enzyme classes with different binding pocket environments, as a representative set of diverse ligands and receptors. Each target contains dozens of different ligands bound to the same binding pocket. We also demonstrate that this flexible docking algorithm may be applicable to RNA docking with a representative riboswitch example. Our findings show significant improvements in top ranking accuracy across this set, with the largest improvement relative to rigid, 23.64%, occurring for ligands binding to DHFR. We then evaluate the ability to identify lead compounds among a large chemical space for the proposed flexible receptor docking algorithm using a subset of the DUD-E containing receptor targets MCR, GCR, and ANDR. We demonstrate that our new algorithms show improved performance in modeling flexible binding site residues compared to DOCK. Finally, we select the T4 L99A and T4 L99A/M102Q decoy sets, containing dozens of binders and experimentally validated nonbinders, to test our approach in distinguishing binders from nonbinders. We illustrate that our new algorithms for searching and scoring have superior performance to rigid receptor CDOCKER as well as AutoDock Vina. Finally, we suggest that flexible CDOCKER is sufficiently fast to be utilized in high-throughput docking screens in the context of hierarchical approaches.

Structure-based drug repurposing against COVID-19 and emerging infectious diseases: methods, resources and discoveries

To attain promising pharmacotherapies, researchers have applied drug repurposing (DR) techniques to discover the candidate medicines to combat the coronavirus disease 2019 (COVID-19) outbreak. Although many DR approaches have been introduced for treating different diseases, only structure-based DR (SBDR) methods can be employed as the first therapeutic option against the COVID-19 pandemic because they rely on the rudimentary information about the diseases such as the sequence of the severe acute respiratory syndrome coronavirus 2 genome. Hence, to try out new treatments for the disease, the first attempts have been made based on the SBDR methods which seem to be among the proper choices for discovering the potential medications against the emerging and re-emerging infectious diseases. Given the importance of SBDR approaches, in the present review, well-known SBDR methods are summarized, and their merits are investigated. Then, the databases and software applications, utilized for repurposing the drugs against COVID-19, are introduced. Besides, the identified drugs are categorized based on their targets. Finally, a comparison is made between the SBDR approaches and other DR methods, and some possible future directions are proposed.

New tale on LianHuaQingWen: IL6R/IL6/IL6ST complex is a potential target for COVID-19 treatment

LianHuaQingWen (LHQW) improves clinical symptoms and alleviates the severity of COVID-19, but the mechanism is unclear. This study aimed to investigate the potential molecular targets and mechanisms of LHQW in treating COVID-19 using a network pharmacology-based approach and molecular docking analysis. The main active ingredients, therapeutic targets of LHQW, and the pathogenic targets of COVID-19 were screened using the TCMSP, UniProt, STRING, and GeneCards databases. According to the “Drug-Ingredients-Targets-Disease” network, Interleukin 6 (IL6) was identified as the core target, and quercetin, luteolin, and wogonin as the active ingredients of LHQW associated with IL6. The response to lipopolysaccharide was the most significant biological process identified by gene ontology enrichment analysis, and AGE-RAGE signaling pathway activation was prominent based on the interaction between LHQW and COVID-19. Protein-protein docking analysis showed that IL6 receptor (IL6R)/IL6/IL6 receptor subunit beta (IL6ST) and Spike protein were mainly bound via conventional hydrogen bonds. Furthermore, protein-small molecule docking showed that all three active ingredients could bind stably in the binding model of IL6R/IL6 and IL6ST. Our findings suggest that LHQW may inhibit the lipopolysaccharide-mediated inflammatory response and regulate the AGE-RAGE signaling pathway through IL6. In addition, the N-terminal domain of the S protein of COVID-19 has a good binding activity to IL6ST, and quercetin and wogonin in LHQW may affect IL6ST-mediated IL6 signal transduction and a large number of signaling pathways downstream to other cytokines by directly affecting protein-protein interaction. These findings suggest the potential molecular mechanism by which LHQW inhibits COVID-19 through the regulation of IL6R/IL6/IL6ST.

Immune Mechanism, Gene Module, and Molecular Subtype Identification of Astragalus Membranaceus in the Treatment of Dilated Cardiomyopathy: An Integrated Bioinformatics Study

Astragalus membranaceus has complex components as a natural drug and has multilevel, multitarget, and multichannel effects on dilated cardiomyopathy (DCM). However, the immune mechanism, gene module, and molecular subtype of astragalus membranaceus in the treatment of DCM are still not revealed. Microarray information of GSE84796 was downloaded from the GEO database, including RNA sequencing data of seven normal cardiac tissues and ten DCM cardiac tissues. A total of 4029 DCM differentially expressed genes were obtained, including 1855 upregulated genes and 2174 downregulated genes. GO/KEGG/GSEA analysis suggested that the activation of T cells and B cells was the primary cause of DCM. WGCNA was used to obtain blue module genes. The blue module genes are primarily ADCY7, BANK1, CD1E, CD19, CD38, CD300LF, CLEC4E, FLT3, GPR18, HCAR3, IRF4, LAMP3, MRC1, SYK, and TLR8, which successfully divided DCM into three molecular subtypes. Based on the CIBERSORT algorithm, the immune infiltration profile of DCM was analyzed. Many immune cell subtypes, including the abovementioned immune cells, showed different levels of increased infiltration in the myocardial tissue of DCM. However, this infiltration pattern was not obviously correlated with clinical characteristics, such as age, EF, and sex. Based on network pharmacology and ClueGO, 20 active components of Astragalus membranaceus and 40 components of DMCTGS were obtained from TCMSP. Through analysis of the immune regulatory network, we found that Astragalus membranaceus effectively regulates the activation of immune cells, such as B cells and T cells, cytokine secretion, and other processes and can intervene in DCM at multiple components, targets, and levels. The above mechanisms were verified by molecular docking results, which confirmed that AKT1, VEGFA, MMP9, and RELA are promising potential targets of DCM.

Synthesis and Biological Evaluation of Honokiol Derivatives Bearing 3-((5-phenyl-1,3,4-oxadiazol-2-yl)methyl)oxazol-2(3H)-ones as Potential Viral Entry Inhibitors against SARS-CoV-2

The 2019 coronavirus disease (COVID-19) caused by SARS-CoV-2 virus infection has posed a serious danger to global health and the economy. However, SARS-CoV-2 medications that are specific and effective are still being developed. Honokiol is a bioactive component from Magnoliae officinalis Cortex with damp-drying effect. To develop new potent antiviral molecules, a series of novel honokiol analogues were synthesized by introducing various 3-((5-phenyl-1,3,4-oxadiazol-2-yl)methyl)oxazol-2(3H)-ones to its molecule. In a SARS-CoV-2 pseudovirus model, all honokiol derivatives were examined for their antiviral entry activities. As a result, 6a and 6p demonstrated antiviral entry effect with IC₅₀ values of 29.23 and 9.82 µM, respectively. However, the parental honokiol had a very weak antiviral activity with an IC₅₀ value more than 50 µM. A biolayer interfero-metry (BLI) binding assay and molecular docking study revealed that 6p binds to human ACE2 protein with higher binding affinity and lower binding energy than the parental honokiol. A competitive ELISA assay confirmed the inhibitory effect of 6p on SARS-CoV-2 spike RBD’s binding with ACE2. Importantly, 6a and 6p (TC₅₀ > 100 μM) also had higher biological safety for host cells than honokiol (TC₅₀ of 48.23 μM). This research may contribute to the discovery of potential viral entrance inhibitors for the SARS-CoV-2 virus, although 6p’s antiviral efficacy needs to be validated on SARS-CoV-2 viral strains in a biosafety level 3 facility.

How much can physics do for protein design?

Physics and physical chemistry are an important thread in computational protein design, complementary to knowledge-based tools. They provide molecular mechanics scoring functions that need little or no ad hoc parameter readjustment, methods to thoroughly sample equilibrium ensembles, and different levels of approximation for conformational flexibility. They led recently to the successful redesign of a small protein using a physics-based folded state energy. Adaptive Monte Carlo or molecular dynamics schemes were discovered where protein variants are populated as per their ligand-binding free energy or catalytic efficiency. Molecular dynamics have been used for backbone flexibility. Implicit solvent models have been refined, polarizable force fields applied, and many physical insights obtained.

Identifying the molecular targets and mechanisms of xuebijing injection for the treatment of COVID-19 via network parmacology and molecular docking

Xuebijing Injection have been found to improve the clinical symptoms of COVID-19 and alleviate disease severity, but the mechanisms are currently unclear. This study aimed to investigate the potential molecular targets and mechanisms of the Xuebijing injection in treating COVID-19 via network pharmacology and molecular docking analysis. The main active ingredients and therapeutic targets of the Xuebijing injection, and the pathogenic targets of COVID-19 were screened using the TCMSP, UniProt, and GeneCard databases. According to the ‘Drug-Ingredients-Targets-Disease’ network built by STRING and Cytoscape, AKT1 was identified as the core target, and baicalein, luteolin, and quercetin were identified as the active ingredients of the Xuebijing injection in connection with AKT1. R language was used for enrichment analysis that predict the mechanisms by which the Xuebijing injection may inhibit lipopolysaccharide-mediated inflammatory response, modulate NOS activity, and regulate the TNF signal pathway by affecting the role of AKT1. Based on the results of network pharmacology, a molecular docking was performed with AKT1 and the three active ingredients, the results indicated that all three active ingredients could stably bind with AKT1. These findings identify potential molecular mechanisms by which Xuebijing Injection inhibit COVID-19 by acting on AKT1.

Mechanistic Exploration of Methionine 274 Acting as a "Switch" of the Selective Pocket Involved in HDAC8 Inhibition: An in Silico Study

The overexpression of histone deacetylase 8 (HDAC8) causes several diseases, and the selective inhibition of HDAC8 has been touted as a promising therapeutic strategy due to its fewer side effects. However, the mechanism of HDAC8 selective inhibition remains unclear. In this study, flexible docking and in silico mutation were used to explore the structural change of methionine (M274) during HDAC8 binding to inhibitors, along with the reason for this change. Meanwhile, steered and conventional molecular dynamics simulations were employed to explore the stability of the structural change. The findings suggest that M274 acts as a "switch" to control the exposure of the HDAC8-selective pocket. The structure of M274 changes from flipped-out to flipped-in only when L-shaped inhibitors bind to HDAC8. This structural change forms a groove that allows these inhibitors to enter the selective pocket. In other HDACs, a leucine residue replaces M274 in situ, and the same structural change is not observed. The findings reveal the mechanism of selective HDAC8 inhibition and provide guidance for the development of novel selective inhibitors.

Discovery of novel Staphylococcus aureus penicillin binding protein 2a inhibitors by multistep virtual screening and biological evaluation

Penicillin-binding protein 2a (PBP2a) is an essential protein involved in the resistance to β-lactam antibiotics acquired by methicillin-resistant Staphylococcus aureus (MRSA) and is a potential antibacterial target. In the current study, we employed a strategy that combined virtual screening with biological evaluation to discover novel inhibitors of PBP2a. In this investigation, a hybrid virtual screening method, consisting of drug-likeness evaluation (Lipinski's Rule of Five and ADMET) and rigid (LibDock) and semi-flexible (CDOCKER) docking-based virtual screenings, was used for retrieving novel PBP2a inhibitors from commercially available chemical databases. 11 compounds were selected from the final hits and subsequently shifted to experimental studies. Among them, Hit 2, Hit 3, and Hit 10 exhibited excellent anti-MRSA ATCC 33591 activity and weak toxicity in vitro. The affinity of the three compounds to bind to PBP2a was further confirmed by surface plasmon resonance (SPR) experiments and molecular dynamics (MD) simulation. An inter-complex interaction study showed that all hit compounds adapted well to the allosteric site of the PBP2a protein. In addition, Hit 2 (with best binding affinity to PBP2a, K_D = 1.29 × 10^-7 M) significantly inhibits proliferation of MRSA clinical isolates. Together, the 3 hit compounds, especially Hit 2, may be potential non-β-lactam antibiotics against MRSA and the work will provide clues for the future development of specific compounds that block the interaction of PBP2a with their targets.

TMPRSS2 inhibitor discovery facilitated through an in silico and biochemical screening platform

The COVID-19 pandemic has highlighted the need for new antiviral targets, as many of the currently approved drugs have proven ineffective against mitigating SARS-CoV-2 infections. The host transmembrane serine protease TMPRSS2 is a highly promising antiviral target, as it plays a direct role in priming the spike protein before viral entry occurs. Further, unlike other targets such as ACE2, TMPRSS2 has no known biological role. Here we utilize virtual screening to curate large libraries into a focused collection of potential inhibitors. Optimization of a recombinant expression and purification protocol for the TMPRSS2 peptidase domain facilitates subsequent biochemical screening and characterization of selected compounds from the curated collection in a kinetic assay. In doing so, we demonstrate that serine protease inhibitors camostat, nafamostat, and gabexate inhibit through a covalent mechanism. We further identify new non-covalent compounds as TMPRSS2 protease inhibitors, demonstrating the utility of a combined virtual and experimental screening campaign in rapid drug discovery efforts.

GPU-Accelerated Flexible Molecular Docking

Virtual screening is a key enabler of computational drug discovery and requires accurate and efficient structure-based molecular docking. In this work, we develop algorithms and software building blocks for molecular docking that can take advantage of graphics processing units (GPUs). Specifically, we focus on MedusaDock, a flexible protein-small molecule docking approach and platform. We accelerate the performance of the coarse docking phase of MedusaDock, as this step constitutes nearly 70% of total running time in typical use-cases. We perform a comprehensive evaluation of the quality and performance with single-GPU and multi-GPU acceleration using a data set of 3875 protein-ligand complexes. The algorithmic ideas, data structure design choices, and performance optimization techniques shed light on GPU acceleration of other structure-based molecular docking software tools.