Validation and use of the MM-PBSA approach for drug discovery

Insights into seed coats of nine cultivars of Australian lupin: Unravelling LC-QTOF MS-based biochemical profiles, nutritional, functional, antioxidant, and antidiabetic properties together with rationalizing antidiabetic mechanism by in silico approaches

Lupins, and other legumes, have attained international interest due to their reported remarkable health benefits. Currently, the seed coats are discarded as waste or animal feed. The research presented here summarizes the potential for incorporating the seed coats into 'whole grain' foods. We aimed to identify metabolites found in the seed coats of nine commercial Australian cultivars of lupin (Lupinus angustifolius and L. albus species), and to evaluate and compare their functional, nutritional, antioxidant, and antidiabetic properties, along with in silico exploration of mechanisms of action for selected identified secondary metabolites. The seed coats were found to contain 79 to 90% dietary fibers and substantial quantity of essential macrometals. LC-QTOF MS-based, untargeted bioactive metabolite profiling explored a total of 673 chemical entities, and identified 63 bioactive secondary metabolites including: biophenols, unsaturated fatty acids, triterpenoids, alkaloids, and dietary prebiotics (insoluble fibers). The seed coats from these nine cultivars show substantial antioxidant activity. The cultivars of L. angustifolius inhibit α-amylase and α-glucosidase significantly in vitro. Moreover, in silico docking and dynamic simulation along with ADME/T analysis suggest that quercetin 3-methyl ether and 8-C-methylquercetin 3-methyl ether as molecules, novel in lupin seed coats, are responsible for the α-amylase and α-glucosidase inhibition. The findings indicated that lupin seed coats might be beneficial food components, rather than be discarded as 'mill waste'.

Structure-Aided Computational Design of Triazole-Based Targeted Covalent Inhibitors of Cruzipain

Cruzipain (CZP), the major cysteine protease present in T. cruzi, the ethiological agent of Chagas disease, has attracted particular attention as a therapeutic target for the development of targeted covalent inhibitors (TCI). The vast chemical space associated with the enormous molecular diversity feasible to explore by means of modern synthetic approaches allows the design of CZP inhibitors capable of exhibiting not only an efficient enzyme inhibition but also an adequate translation to anti-T. cruzi activity. In this work, a computer-aided design strategy was developed to combinatorially construct and screen large libraries of 1,4-disubstituted 1,2,3-triazole analogues, further identifying a selected set of candidates for advancement towards synthetic and biological activity evaluation stages. In this way, a virtual molecular library comprising more than 75 thousand diverse and synthetically feasible analogues was studied by means of molecular docking and molecular dynamic simulations in the search of potential TCI of CZP, guiding the synthetic efforts towards a subset of 48 candidates. These were synthesized by applying a Cu(I)-catalyzed azide-alkyne cycloaddition (CuAAC) centered synthetic scheme, resulting in moderate to good yields and leading to the identification of 12 hits selectively inhibiting CZP activity with IC50 in the low micromolar range. Furthermore, four triazole derivatives showed good anti-T. cruzi inhibition when studied at 50 μM; and Ald-6 excelled for its high antitrypanocidal activity and low cytotoxicity, exhibiting complete in vitro biological activity translation from CZP to T. cruzi. Overall, not only Ald-6 merits further advancement to preclinical in vivo studies, but these findings also shed light on a valuable chemical space where molecular diversity might be explored in the search for efficient triazole-based antichagasic agents.

Reinforcement learning-driven exploration of peptide space: accelerating generation of drug-like peptides

Using amino acid residues in peptide generation has solved several key problems, including precise control of amino acid sequence order, customized peptides for property modification, and large-scale peptide synthesis. Proteins contain unknown amino acid residues. Extracting them for the synthesis of drug-like peptides can create novel structures with unique properties, driving drug development. Computer-aided design of novel peptide drug molecules can solve the high-cost and low-efficiency problems in the traditional drug discovery process. Previous studies faced limitations in enhancing the bioactivity and drug-likeness of polypeptide drugs due to less emphasis on the connection relationships in amino acid structures. Thus, we proposed a reinforcement learning-driven generation model based on graph attention mechanisms for peptide generation. By harnessing the advantages of graph attention mechanisms, this model effectively captured the connectivity structures between amino acid residues in peptides. Simultaneously, leveraging reinforcement learning's strength in guiding optimal sequence searches provided a novel approach to peptide design and optimization. This model introduces an actor-critic framework with real-time feedback loops to achieve dynamic balance between attributes, which can customize the generation of multiple peptides for specific targets and enhance the affinity between peptides and targets. Experimental results demonstrate that the generated drug-like peptides meet specified absorption, distribution, metabolism, excretion, and toxicity properties and bioactivity with a success rate of over 90$\%$, thereby significantly accelerating the process of drug-like peptide generation.

In silico fragment-based discovery of CIB1-directed anti-tumor agents by FRASE-bot

Chemical probes are an indispensable tool for translating biological discoveries into new therapies, though are increasingly difficult to identify since novel therapeutic targets are often hard-to-drug proteins. We introduce FRASE-based hit-finding robot (FRASE-bot), to expedite drug discovery for unconventional therapeutic targets. FRASE-bot mines available 3D structures of ligand-protein complexes to create a database of FRAgments in Structural Environments (FRASE). The FRASE database can be screened to identify structural environments similar to those in the target protein and seed the target structure with relevant ligand fragments. A neural network model is used to retain fragments with the highest likelihood of being native binders. The seeded fragments then inform ultra-large-scale virtual screening of commercially available compounds. We apply FRASE-bot to identify ligands for Calcium and Integrin Binding protein 1 (CIB1), a promising drug target implicated in triple negative breast cancer. FRASE-based virtual screening identifies a small-molecule CIB1 ligand (with binding confirmed in a TR-FRET assay) showing specific cell-killing activity in CIB1-dependent cancer cells, but not in CIB1-depletion-insensitive cells.

Identification of novel c-Kit inhibitors from natural sources using virtual screening and molecular dynamics simulations

The Mast/Stem cell growth factor receptor Kit (c-Kit), a Proto-oncogene c-Kit, is a tyrosine-protein kinase involved in cell differentiation, proliferation, migration, and survival. Its role in developing certain cancers, particularly gastrointestinal stromal tumors (GISTs) and acute myeloid leukemia (AML), makes it an attractive therapeutic target. Several small molecule inhibitors targeting c-Kit have been developed and approved for clinical use. Recent studies have focused on identifying and optimizing natural compounds as c-Kit inhibitors employing virtual screening. Still, drug resistance, off-target side effects, and variability in patient response remain significant challenges. From this perspective, phytochemicals could be an important resource for discovering novel c-Kit inhibitors with less toxicity, improved efficacy, and high specificity. This study aimed to uncover possible c-Kit inhibitors by utilizing a structure-based virtual screening of active phytoconstituents from Indian medicinal plants. Through the screening stages, two promising candidates, Anilinonaphthalene and Licoflavonol, were chosen based on their drug-like features and ability to bind to c-Kit. These chosen candidates were subjected to all-atom molecular dynamics (MD) simulations to evaluate their stability and interaction with c-Kit. The selected compounds Anilinonaphthalene from Daucus carota and Licoflavonol from Glycyrrhiza glabra showed their potential to act as selective binding partners of c-Kit. Our results suggest that the identified phytoconstituents could serve as a starting point to develop novel c-Kit inhibitors for developing new and effective therapies against multiple cancers, including GISTs and AML. The use of virtual screening and MD simulations provides a rational approach to discovering potential drug candidates from natural sources.Communicated by Ramaswamy H. Sarma.

Shedding Light on Dark Chemical Matter: The Discovery of a SARS-CoV-2 Mpro Main Protease Inhibitor through Intensive Virtual Screening and In Vitro Evaluation

The development of specific antiviral therapies targeting SARS-CoV-2 remains fundamental because of the continued high incidence of COVID-19 and limited accessibility to antivirals in some countries. In this context, dark chemical matter (DCM), a set of drug-like compounds with outstanding selectivity profiles that have never shown bioactivity despite being extensively assayed, appears to be an excellent starting point for drug development. Accordingly, in this study, we performed a high-throughput screening to identify inhibitors of the SARS-CoV-2 main protease (Mpro) using DCM compounds as ligands. Multiple receptors and two different docking scoring functions were employed to identify the best molecular docking poses. The selected structures were subjected to extensive conventional and Gaussian accelerated molecular dynamics. From the results, four compounds with the best molecular behavior and binding energy were selected for experimental testing, one of which presented inhibitory activity with a Ki value of 48 ± 5 μM. Through virtual screening, we identified a significant starting point for drug development, shedding new light on DCM compounds.

Efficient Refinement of Complex Structures of Flexible Histone Peptides Using Post-Docking Molecular Dynamics Protocols

Histones are keys to many epigenetic events and their complexes have therapeutic and diagnostic importance. The determination of the structures of histone complexes is fundamental in the design of new drugs. Computational molecular docking is widely used for the prediction of target-ligand complexes. Large, linear peptides like the tail regions of histones are challenging ligands for docking due to their large conformational flexibility, extensive hydration, and weak interactions with the shallow binding pockets of their reader proteins. Thus, fast docking methods often fail to produce complex structures of such peptide ligands at a level appropriate for drug design. To address this challenge, and improve the structural quality of the docked complexes, post-docking refinement has been applied using various molecular dynamics (MD) approaches. However, a final consensus has not been reached on the desired MD refinement protocol. In this present study, MD refinement strategies were systematically explored on a set of problematic complexes of histone peptide ligands with relatively large errors in their docked geometries. Six protocols were compared that differ in their MD simulation parameters. In all cases, pre-MD hydration of the complex interface regions was applied to avoid the unwanted presence of empty cavities. The best-performing protocol achieved a median of 32% improvement over the docked structures in terms of the change in root mean squared deviations from the experimental references. The influence of structural factors and explicit hydration on the performance of post-docking MD refinements are also discussed to help with their implementation in future methods and applications.

Comparison of interactions between alpha-lactalbumin and three protopanaxadiol ginsenosides: Impacts on the structure and antitumor properties

In this research, interactions between α-lactalbumin (ALA) and three protopanaxadiol ginsenosides [20(S)-Rg3, 20(S)-Rh2, and 20(S)-PPD] were compared to explore the effects of similar ligand on structure and cytotoxicity of ALA. Multi-spectroscopy revealed the binding between ALA and ginsenoside changed the conformation of ALA, which related to different structures and solubility of ligands. Scanning electron microscope illustrated that all ALA-ginsenoside complexes exhibited denser structures via hydrophobic interactions. Additionally, the cytotoxic experiments confirmed that the cytotoxicity of ginsenoside was enhanced after binding with ALA. Molecular docking showed all three ginsenosides were bound to the sulcus depression region of ALA via hydrogen bonding and hydrophobic interaction. Furthermore, molecular dynamics simulation elucidated the precise binding sites and pertinent system properties. Among all three composite systems, 20(S)-Rh2 had optimal binding affinity. These findings enhanced understanding of the synergistic utilization of ALA and ginsenosides as functional ingredients in food, medicine, and cosmetics.

Integration of ligand and structure-based pharmacophore screening for the identification of novel natural leads against Euchromatic histone lysine methyltransferase 2 (EHMT2/G9a)

Herein, we report a blended ligand and structure-based pharmacophore screening approach to identify new natural leads against the Protein Lysine Methyltransferase 2 (EHMT2/G9a). The EHMT2/G9a has been associated with Cancer, Alzheimer's, and aging and is considered an emerging drug target having no clinically passed inhibitor. Purposefully, we developed the ligand-based pharmacophore (Pharmacophore-L) based on the common features of known inhibitors and the structure-based pharmacophore (Pharmacophore-S) based on the interaction profile of available crystal structures. The Pharmacophore-L and Pharmacophore-S were subjected to multiple tiers of validations and utilized in combination for the screening of total 741543 compounds coming from multiple databases. Additional layers of stringency were applied in the screening process to test drug-likeness (using Lipinski's rule, Veber's rule, SMARTS and ADMET filtration), to rule out any toxicity (TOPKAT analysis). The interaction profiles, stabilities, and comparative analysis against the reference were carried out by flexible docking, MD simulation, and MM-GBSA analysis, which finally led to three leads as potential inhibitors of G9a.Communicated by Ramaswamy H. Sarma.

The Discovery of inhibitors of the SARS-CoV-2 S protein through computational drug repurposing

SARS-CoV-2 must bind its principal receptor, ACE2, on the target cell to initiate infection. This interaction is largely driven by the receptor binding domain (RBD) of the viral Spike (S) protein. Accordingly, antiviral compounds that can block RBD/ACE2 interactions can constitute promising antiviral agents. To identify such molecules, we performed a virtual screening of the Selleck FDA approved drugs and the Selleck database of Natural Products using a multistep computational procedure. An initial set of candidates was identified from an ensemble docking process using representative structures determined from the analysis of four 3 μ s molecular dynamics trajectories of the RBD/ACE2 complex. Two procedures were used to construct an initial set of candidates including a standard and a pharmacophore guided docking procedure. The initial set was subsequently subjected to a multistep sieving process to reduce the number of candidates to be tested experimentally, using increasingly demanding computational procedures, including the calculation of the binding free energy computed using the MMPBSA and MMGBSA methods. After the sieving process, a final list of 10 candidates was proposed, compounds which were subsequently purchased and tested ex-vivo. The results identified estradiol cypionate and telmisartan as inhibitors of SARS-CoV-2 entry into cells. Our findings demonstrate that the methodology presented here enables the discovery of inhibitors targeting viruses for which high-resolution structures are available.

Revealing the binding mechanism of BACE1 inhibitors through molecular dynamics simulations

Alzheimer's disease is a debilitating neurodegenerative disorder, and the Beta-Site Amyloid Precursor Protein Cleaving Enzyme 1 (BACE1) is a key therapeutic target in its treatment. This study employs molecular dynamics simulations and binding energy analysis to investigate the binding interactions between BACE1 and four selected small molecules: CNP520, D9W, NB641, and NB360. The binding model analysis indicates that the binding of BACE1 with four molecules are stable, except the loop regions show significant fluctuation. The binding free energy analyses reveal that NB360 exhibits the highest binding affinity with BACE1, surpassing other molecules (CNP520, D9W, and NB641). Detailed energy component assessments highlight the critical roles of electrostatic interactions and van der Waals forces in the binding process. Furthermore, residue contribution analysis identifies key amino acids influencing the binding process across all systems. Hydrogen bond analysis reveals a limited number of bonds between BACE1 and each small molecule, highlighting the importance of structural modifications to enable more stable hydrogen bonds. This research provides valuable insights into the molecular mechanisms of potential Alzheimer's disease therapeutics, guiding the way for improved drug design and the development of effective treatments targeting BACE1.Communicated by Ramaswamy H. Sarma.

Exploring protein-ligand binding affinity prediction with electron density-based geometric deep learning

Rational structure-based drug design relies on accurate predictions of protein-ligand binding affinity from structural molecular information. Although deep learning-based methods for predicting binding affinity have shown promise in computational drug design, certain approaches have faced criticism for their potential to inadequately capture the fundamental physical interactions between ligands and their macromolecular targets or for being susceptible to dataset biases. Herein, we propose to include bond-critical points based on the electron density of a protein-ligand complex as a fundamental physical representation of protein-ligand interactions. Employing a geometric deep learning model, we explore the usefulness of these bond-critical points to predict absolute binding affinities of protein-ligand complexes, benchmark model performance against existing methods, and provide a critical analysis of this new approach. The models achieved root-mean-squared errors of 1.4-1.8 log units on the PDBbind dataset, and 1.0-1.7 log units on the PDE10A dataset, not indicating significant advantages over benchmark methods, and thus rendering the utility of electron density for deep learning models context-dependent. The relationship between intermolecular electron density and corresponding binding affinity was analyzed, and Pearson correlation coefficients r > 0.7 were obtained for several macromolecular targets.

Structure-based virtual screening and molecular dynamics studies to explore potential natural inhibitors against 3C protease of foot-and-mouth disease virus

Foot-and-mouth disease (FMD) is a highly infectious animal disease caused by foot-and-mouth disease virus (FMDV) and primarily infects cloven-hoofed animals such as cattle, sheep, goats, and pigs. It has become a significant health concern in global livestock industries because of diverse serotypes, high mutation rates, and contagious nature. There is no specific antiviral treatment available for FMD. Hence, based on the importance of 3C protease in FMDV viral replication and pathogenesis, we have employed a structure-based virtual screening method by targeting 3C protease with a natural compounds dataset (n = 69,040) from the InterBioScreen database. Virtual screening results identified five potential compounds, STOCK1N-62634, STOCK1N-96109, STOCK1N-94672, STOCK1N-89819, and STOCK1N-80570, with a binding affinity of -9.576 kcal/mol, -8.1 kcal/mol, -7.744 kcal/mol, -7.647 kcal/mol, and - 7.778 kcal/mol, respectively. The compounds were further validated through physiochemical properties and density functional theory (DFT). Subsequently, the comparative 300-ns MD simulation of all five complexes exhibited overall structural stability from various MD analyses such as root mean square deviation (RMSD), root mean square fluctuation (RMSF), radius of gyration (Rg), solvent accessible surface area (SASA), H-bonds, principal component analysis (PCA), and free energy landscape (FEL). Furthermore, MM-PBSA calculation suggests that all five compounds, particularly STOCK1N-62634, STOCK1N-96109, and STOCK1N-94672, can be considered as potential inhibitors because of their strong binding affinity toward 3C protease. Thus, we hope that these identified compounds can be studied extensively to develop natural therapeutics for the better management of FMD.

Binding of Cholesterol to the N-Terminal Domain of the NPC1L1 Transporter: Analysis of the Epimerization-Related Binding Selectivity and Loop Mutations

Cholesterol is a fat-like substance with a pivotal physiological relevance in humans, and its homeostasis is tightly regulated by various cellular processes, including the import in the small intestine and the reabsorption in the biliary ducts by the Niemann-Pick C1 Like 1 (NPC1L1) importer. NPC1L1 can mediate the absorption of a variety of sterols but strikingly exhibits a large sensitivity to cholesterol epimerization. This study examines the molecular basis of the epimerization-related selective binding of cholesterol by combining extended unbiased molecular dynamics simulations of the apo and holo species of the N-terminal domain of wild-type NPC1L1, in conjunction with relative binding free energy, umbrella sampling, and well-tempered metadynamics calculations. The analysis of the results discloses the existence of two distinct binding modes for cholesterol and epi-cholesterol. The former binds deeper in the cavity, forming key hydrogen-bond interactions with Q95, S56, and a water molecule. In contrast, epi-cholesterol is shifted ca. 3 Å to the mouth of the cavity and the transition to the Q95 site is prevented by an energetic barrier of 4.1 kcal·mol-1. Thus, the configuration of the hydroxyl group of cholesterol, together with the presence of a structural water molecule, is a key feature for effective absorption. Finally, whereas these findings may seemingly be challenged by single-point mutations that impair cholesterol transport but have a mild impact on the binding of cholesterol to the Q95 binding site, our results reveal that they have a drastic influence on the conformational landscape of the α8/β7 loop in the apo species compared to the wild-type protein. Overall, the results give support to the functional role played by the α8/β7 loop in regulating the access of ligands to NPC1L1, and hence to interpreting the impact of these mutations on diseases related to disruption of sterol absorption, paving the way to understanding certain physiological dysfunctions.

Identification of New GSK3β Inhibitors through a Consensus Machine Learning-Based Virtual Screening

Glycogen synthase kinase-3 beta (GSK3β) is a serine/threonine kinase that plays key roles in glycogen metabolism, Wnt/β-catenin signaling cascade, synaptic modulation, and multiple autophagy-related signaling pathways. GSK3β is an attractive target for drug discovery since its aberrant activity is involved in the development of neurodegenerative diseases such as Alzheimer's and Parkinson's disease. In the present study, multiple machine learning models aimed at identifying novel GSK3β inhibitors were developed and evaluated for their predictive reliability. The most powerful models were combined in a consensus approach, which was used to screen about 2 million commercial compounds. Our consensus machine learning-based virtual screening led to the identification of compounds G1 and G4, which showed inhibitory activity against GSK3β in the low-micromolar and sub-micromolar range, respectively. These results demonstrated the reliability of our virtual screening approach. Moreover, docking and molecular dynamics simulation studies were employed for predicting reliable binding modes for G1 and G4, which represent two valuable starting points for future hit-to-lead and lead optimization studies.

Discovery of Potent and Selective CB2 Agonists Utilizing a Function-Based Computational Screening Protocol

Nowadays, the identification of agonists and antagonists represents a great challenge in computer-aided drug design. In this work, we developed a computational protocol enabling us to design/screen novel chemicals that are likely to serve as selective CB2 agonists. The principle of this protocol is that by calculating the ligand-residue interaction profile (LRIP) of a ligand binding to a specific target, the agonist-antagonist function of a compound is then able to be determined after statistical analysis and free energy calculations. This computational protocol was successfully applied in CB2 agonist development starting from a lead compound, and a success rate of 70% was achieved. The functions of the synthesized derivatives were determined by in vitro functional assays. Moreover, the identified potent CB2 agonists and antagonists strongly interact with the key residues identified using the already known potent CB2 agonists/antagonists. The analysis of the interaction profile of compound 6, a potent agonist, showed strong interactions with F2.61, I186, and F2.64, while compound 39, a potent antagonist, showed strong interactions with L17, W6.48, V6.51, and C7.42. Still, some residues including V3.32, T3.33, S7.39, F183, W5.43, and I3.29 are hotspots for both CB2 agonists and antagonists. More significantly, we identified three hotspot residues in the loop, including I186 for agonists, L17 for antagonists, and F183 for both. These hotspot residues are typically not considered in CB1/CB2 rational ligand design. In conclusion, LRIP is a useful concept in rationally designing a compound to possess a certain function.

A comprehension on structure guided alignment dependent 3D-QSAR modelling, and molecular dynamics simulation on 2,4-thiazolidinediones as aldose reductase inhibitors for the management of diabetic complications

Aldose reductase is an oxo-reductase enzyme belonging to the aldo-keto reductase class. Compounds having thiazolidine-2,4-dione scaffold are reported as potential aldose reductase inhibitors for diabetic complications. The present work uses structure-guided alignment-dependent Gaussian field- and atom-based 3D-QSAR on a dataset of 84 molecules. 3D-QSAR studies on two sets of dataset alignment have been carried out to understand the favourable and unfavourable structural features influencing the affinity of these inhibitors towards the enzyme. Using common pharmacophore hypotheses, the five-point pharmacophores for aldose reductase favourable features were generated. The molecular dynamics simulations (up to 100 ns) were performed for the potent molecule from each alignment set (compounds 24 and 65) compared to reference standard tolrestat and epalrestat to study target-ligand complexes' binding energy and stability. Compound 65 was most stable with better interactions in the aldose reductase binding pocket than tolrestat. The MM-PBSA study suggests compound 65 possessed better binding energy than reference standard tolrestat, i.e. -87.437 ± 19.728 and -73.424 ± 12.502 kJ/mol, respectively. The generated 3D-QSAR models provide information about structure-activity relationships and ligand-target binding energy. Target-specific stability data from MD simulation would be helpful for rational compound design with better aldose reductase activity.Communicated by Ramaswamy H. Sarma.

Prediction of molecular interaction of Phosphodiesterase 10A inhibition by natural compounds: insights from structure-based screening and molecular dynamics simulations

Phosphodiesterase 10 A (PDE10A) is an enzyme that regulates cyclic adenosine monophosphate (cAMP) and cyclic guanosine monophosphate (cGMP) levels in the brain, particularly in the striatum, which plays a critical role in movement control and reward processing. Inhibition of PDE10A can increase cAMP and cGMP levels, improving neuronal signaling and reducing symptoms of neuropsychiatric disorders such as schizophrenia, Huntington's disease, and Parkinson's disease. In this study, a structure-based virtual screening was conducted to identify potential anti-neuropsychiatric disorders compounds from phytoconstituents in the IMPPAT database. The ligands were docked against PDE10A, resulting in 40 compounds with appreciable docking scores. These 40 compounds underwent further ADMET predictions and drug likeliness, resulting in five potential compounds. Finally, based on the specific interactions, two compounds (Colladonin and Isopongachromene), were subjected to molecular dynamics (MD) simulation and MM-PBSA studies. The MM-PBSA analysis validated and captured the intermolecular interactions, indicating that Colladonin and Isopongachromene had appreciable binding affinities of -155.60 kJ.mol-1 and -108.28 kJ.mol-1, respectively and were promising candidates against neuropsychiatric disorders, targeting PDE10A. Overall, this study provides insight into the potential of PDE10A inhibitors as therapeutic agents for treating neuropsychiatric disorders, and Colladonin and Isopongachromene are promising compounds for further development.Communicated by Ramaswamy H. Sarma.

Exploring natural compound, Panicutine as leucine-rich repeat kinase 2 inhibitor against Parkinson's disease: a structure-guided approach

Leucine-rich repeat kinase 2 (LRRK2) is a promising drug target for the therapeutic management of Parkinson's disease (PD) and other neurodegenerative disorders. LRRK2 inhibitors have the potential to modulate neuroinflammation, reduce alpha-synuclein aggregation and improve motor symptoms in PD patients. Although LRRK2 inhibitors are still in the early stages of clinical development, the identification of potent and selective inhibitors through structure-guided approaches provides a promising avenue for the development of effective therapies for PD and other neurodegenerative disorders. In this study, natural compounds from the IMPPAT database were screened using a state-of-the-art computational virtual screening approach to identify potential inhibitors of LRRK2. We carried out a docking screening on a library of natural compounds and identified a few compounds with strong binding affinity, docking score and specificity towards LRRK2 as the top hits. These hits were then subjected to further analysis based on multiple parameters for the Pan-assay interference compounds and their physicochemical and pharmacokinetics evaluation followed by a detailed interaction analysis. After careful evaluation, one natural compound, Panicutine, was identified as a promising candidate for LRRK2 due to its significant affinity and specificity towards the LRRK2 binding pocket. Additionally, it exhibited drug-like properties with blood-brain barrier permeability as determined by ADMET properties. To gain a deeper understanding of the stability and conformational changes of the LRRK2-ligand complex, MD simulations were conducted for 100 nanoseconds under explicit solvent conditions followed by principal component analysis and free energy dynamics. The simulation results demonstrated that the LRRK2-Panicutine complex remained stable throughout the simulation trajectories. Based on these findings, it is concluded that Panicutine has the potential to act as a LRRK2 inhibitor against PD and other neurodegenerative disorders.Communicated by Ramaswamy H. Sarma.

Design, synthesis, antiviral evaluation, and In silico studies of acrylamides targeting nsP2 from Chikungunya virus

The Togaviridae family comprises several New- and Old-World Alphaviruses that have been responsible for thousands of human illnesses, including the RNA arbovirus Chikungunya virus (CHIKV). Firstly, it was reported in Tanzania in 1952 but rapidly it spread to several countries from Europe, Asia, and the Americas. Since then, CHIKV has been circulating in diverse countries around the world, leading to increased morbidity rates. Currently, there are no FDA-approved drugs or licensed vaccines to specifically treat CHIKV infections. Thus, there is a lack of alternatives to fight against this viral disease, making it an unmet need. Structurally, CHIKV is composed of five structural proteins (E3, E2, E1, C, and 6k) and four non-structural proteins (nsP1-4), in which nsP2 represents an attractive antiviral target for designing novel inhibitors since it has an essential role in the virus replication and transcription. Herein, we used a rational drug design strategy to select some acrylamide derivatives to be synthesized and evaluated against CHIKV nsP2 and also screened on CHIKV-infected cells. Thus, two regions of modifications were considered for these types of inhibitors, based on a previous study of our group, generating 1560 possible inhibitors. Then, the 24 most promising ones were synthesized and screened by using a FRET-based enzymatic assay protocol targeting CHIKV nsP2, identifying LQM330, 333, 336, and 338 as the most potent inhibitors, with Ki values of 48.6 ± 2.8, 92.3 ± 1.4, 2.3 ± 1.5, and 181.8 ± 2.5 μM, respectively. Still, their Km and Vmax kinetic parameters were also determined, along with their competitive binding modes of CHIKV nsP2 inhibition. Then, ITC analyses revealed KD values of 127, 159, 198, and 218 μM for LQM330, 333, 336, and 338, respectively. Also, their ΔH, ΔS, and ΔG physicochemical parameters were determined. MD simulations demonstrated that these inhibitors present a stable binding mode with nsP2, interacting with important residues of this protease, according to docking analyzes. Moreover, MM/PBSA calculations displayed that van der Waals interactions are mainly responsible for stabilizing the inhibitor-nsP2 complex, and their binding energies corroborated with their Ki values, having -198.7 ± 15.68, -124.8 ± 17.27, -247.4 ± 23.78, and -100.6 ± 19.21 kcal/mol for LQM330, 333, 336, and 338, respectively. Since Sindbis (SINV) nsP2 is similar to CHIKV nsP2, these best inhibitors were screened against SINV-infected cells, and it was verified that LQM330 presented the best result, with an EC50 value of 0.95 ± 0.09 μM. Even at 50 μM concentration, LQM338 was found to be cytotoxic on Vero cells after 48 h. Then, LQM330, 333, and 336 were evaluated against CHIKV-infected cells in antiviral assays, in which LQM330 was found to be the most promising antiviral candidate in this study, exhibiting an EC50 value of 5.2 ± 0.52 μM and SI of 31.78. The intracellular flow cytometry demonstrated that LQM330 is able to reduce the CHIKV cytopathogenic effect on cells, and also reduce the percentage of CHIKV-positive cells from 66.1% ± 7.05 to 35.8% ± 5.78 at 50 μM concentration. Finally, qPCR studies demonstrated that LQM330 was capable of reducing the number of viral RNA copies/μL, suggesting that CHIKV nsP2 is targeted by this inhibitor as its mechanism of action.

Identification of histidine kinase inhibitors through screening of natural compounds to combat mastitis caused by Streptococcus agalactiae in dairy cattle

BACKGROUND

Mastitis poses a major threat to dairy farms globally; it results in reduced milk production, increased treatment costs, untimely compromised genetic potential, animal deaths, and economic losses. Streptococcus agalactiae is a highly virulent bacteria that cause mastitis. The administration of antibiotics for the treatment of this infection is not advised due to concerns about the emergence of antibiotic resistance and potential adverse effects on human health. Thus, there is a critical need to identify new therapeutic approaches to combat mastitis. One promising target for the development of antibacterial therapies is the transmembrane histidine kinase of bacteria, which plays a key role in signal transduction pathways, secretion systems, virulence, and antibiotic resistance.

RESULTS

In this study, we aimed to identify novel natural compounds that can inhibit transmembrane histidine kinase. To achieve this goal, we conducted a virtual screening of 224,205 natural compounds, selecting the top ten based on their lowest binding energy and favorable protein-ligand interactions. Furthermore, molecular docking of eight selected antibiotics and five histidine kinase inhibitors with transmembrane histidine kinase was performed to evaluate the binding energy with respect to top-screened natural compounds. We also analyzed the ADMET properties of these compounds to assess their drug-likeness. The top two compounds (ZINC000085569031 and ZINC000257435291) and top-screened antibiotics (Tetracycline) that demonstrated a strong binding affinity were subjected to molecular dynamics simulations (100 ns), free energy landscape, and binding free energy calculations using the MM-PBSA method.

CONCLUSION

Our results suggest that the selected natural compounds have the potential to serve as effective inhibitors of transmembrane histidine kinase and can be utilized for the development of novel antibacterial veterinary medicine for mastitis after further validation through clinical studies.

FDW028, a novel FUT8 inhibitor, impels lysosomal proteolysis of B7-H3 via chaperone-mediated autophagy pathway and exhibits potent efficacy against metastatic colorectal cancer

Metastatic colorectal cancer (mCRC) is a major cause of cancer-related mortality due to the absence of effective therapeutics. Thus, it is urgent to discover new drugs for mCRC. Fucosyltransferase 8 (FUT8) is a potential therapeutic target with high level in most malignant cancers including CRC. FUT8 mediates the core fucosylation of CD276 (B7-H3), a key immune checkpoint molecule (ICM), in CRC. FUT8-silence-induced defucosylation at N104 on B7-H3 attracts heat shock protein family A member 8 (HSPA8, also known as HSC70) to bind with 106-110 SLRLQ motif and consequently propels lysosomal proteolysis of B7-H3 through the chaperone-mediated autophagy (CMA) pathway. Then we report the development and characterization of a potent and highly selective small-molecule inhibitor of FUT8, named FDW028, which evidently prolongs the survival of mice with CRC pulmonary metastases (CRPM). FDW028 exhibits potent anti-tumor activity by defucosylation and impelling lysosomal degradation of B7-H3 through the CMA pathway. Taken together, FUT8 inhibition destabilizes B7-H3 through CMA-mediated lysosomal proteolysis, and FDW028 acts as a potent therapeutic candidate against mCRC by targeting FUT8. FDW028, an inhibitor specifically targeted FUT8, promotes defucosylation and consequent HSC70/LAMP2A-mediated lysosomal degradation of B7-H3, and exhibits potent anti-mCRC activities.

Predicting Protein-Peptide Interactions: Benchmarking Deep Learning Techniques and a Comparison with Focused Docking

The accurate prediction of protein structures achieved by deep learning (DL) methods is a significant milestone and has deeply impacted structural biology. Shortly after its release, AlphaFold2 has been evaluated for predicting protein-peptide interactions and shown to significantly outperform RoseTTAfold as well as a conventional blind docking method: PIPER-FlexPepDock. Since then, new AlphaFold2 models, trained specifically to predict multimeric assemblies, have been released and a new ab initio folding model OmegaFold has become available. Here, we assess docking success rates for these new DL folding models and compare their performance with our state-of-the-art, focused peptide-docking software AutoDock CrankPep (ADCP). The evaluation is done using the same dataset and performance metric for all methods. We show that, for a set of 99 nonredundant protein-peptide complexes, the new AlphaFold2 model outperforms other Deep Learning approaches and achieves remarkable docking success rates for peptides. While the docking success rate of ADCP is more modest when considering the top-ranking solution only, it samples correct solutions for around 62% of the complexes. Interestingly, different methods succeed on different complexes, and we describe a consensus docking approach using ADCP and AlphaFold2, which achieves a remarkable 60% for the top-ranking results and 66% for the top 5 results for this set of 99 protein-peptide complexes.

Targeting the PEDV 3CL protease for identification of small molecule inhibitors: an insight from virtual screening, ADMET prediction, molecular dynamics, free energy landscape, and binding energy calculations

BACKGROUND

The porcine epidemic diarrhea virus (PEDV) represents a major health issue for piglets worldwide and does significant damage to the pork industry. Thus, new therapeutic approaches are urgently needed to manage PEDV infections. Due to the current lack of a reliable remedy, this present study aims to identify novel compounds that inhibit the 3CL protease of the virus involved in replication and pathogenesis.

RESULTS

To identify potent antiviral compounds against the 3CL protease, a virtual screening of natural compounds (n = 97,999) was conducted. The top 10 compounds were selected based on the lowest binding energy and the protein-ligand interaction analyzed. Further, the top five compounds that demonstrated a strong binding affinity were subjected to drug-likeness analysis using the ADMET prediction, which was followed by molecular dynamics simulations (500 ns), free energy landscape, and binding free energy calculations using the MM-PBSA method. Based on these parameters, four putative lead (ZINC38167083, ZINC09517223, ZINC04339983, and ZINC09517238) compounds were identified that represent potentially effective inhibitors of the 3CL protease.

CONCLUSION

Therefore, these can be utilized for the development of novel antiviral drugs against PEDV. However, this requires further validation through in vitro and in vivo studies.

Autochthonous Peruvian Natural Plants as Potential SARS-CoV-2 Mpro Main Protease Inhibitors

Over 750 million cases of COVID-19, caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), have been reported since the onset of the global outbreak. The need for effective treatments has spurred intensive research for therapeutic agents based on pharmaceutical repositioning or natural products. In light of prior studies asserting the bioactivity of natural compounds of the autochthonous Peruvian flora, the present study focuses on the identification SARS-CoV-2 M^pro main protease dimer inhibitors. To this end, a target-based virtual screening was performed over a representative set of Peruvian flora-derived natural compounds. The best poses obtained from the ensemble molecular docking process were selected. These structures were subjected to extensive molecular dynamics steps for the computation of binding free energies along the trajectory and evaluation of the stability of the complexes. The compounds exhibiting the best free energy behaviors were selected for in vitro testing, confirming the inhibitory activity of Hyperoside against M^pro, with a K_i value lower than 20 µM, presumably through allosteric modulation.

3D-QSAR, molecular docking, and molecular dynamics analysis of dihydrodiazaindolone derivatives as PARP-1 inhibitors

CONTEXT

PARP-1 plays an important role in DNA repair and apoptosis, and PARP-1 inhibitors have shown to be effective in the treatment of several malignancies. To evaluate the function of new PARP-1 inhibitors as anticancer adjuvant medicines, 3D-QSAR, molecular docking, and molecular dynamics (MD) simulations of a sequence of dihydrodiazepinoindolone derivatives PARP-1 inhibitors were undertaken in this study.

METHODS

In this paper, 43 PARP-1 inhibitors were studied in a three-dimensional quantitative structure-activity relationship (3D-QSAR) using comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA). CoMFA with q2 of 0.675 and r2 of 0.981 was achieved, as was CoMSIA with q2 of 0.755 and r2 of 0.992. The changed areas of these compounds are shown by steric, electrostatic, hydrophobic, and hydrogen-bonded acceptor field contour maps. Subsequently, molecular docking, and molecular dynamics simulations further confirmed that key residues Gly863 and Ser904 of PARP-1 are vital residues for protein interactions and their binding affinity. The effects of 3D-QSAR, molecular docking and molecular dynamics simulations supply a new route for the search of new PARP-1 inhibitors. Finally, we designed eight new compounds with exact activity and ADME/T properties.

Integrated Computational Approaches for Inhibiting Sex Hormone-Binding Globulin in Male Infertility by Screening Potent Phytochemicals

Male infertility is significantly influenced by the plasma-protein sex hormone-binding globulin (SHBG). Male infertility, erectile dysfunction, prostate cancer, and several other male reproductive system diseases are all caused by reduced testosterone bioavailability due to its binding to SHBG. In this study, we have identified 345 phytochemicals from 200 literature reviews that potentially inhibit severe acute respiratory syndrome coronavirus 2. Only a few studies have been done using the SARS-CoV-2 inhibitors to identify the SHBG inhibitor, which is thought to be the main protein responsible for male infertility. In virtual-screening and molecular-docking experiments, cryptomisrine, dorsilurin E, and isoiguesterin were identified as potential SHBG inhibitors with binding affinities of -9.2, -9.0, and -8.8 kcal/mol, respectively. They were also found to have higher binding affinities than the control drug anastrozole (-7.0 kcal/mol). In addition to favorable pharmacological properties, these top three phytochemicals showed no adverse effects in pharmacokinetic evaluations. Several molecular dynamics simulation profiles' root-mean-square deviation, radius of gyration, root-mean-square fluctuation, hydrogen bonds, and solvent-accessible surface area supported the top three protein-ligand complexes' better firmness and stability than the control drug throughout the 100 ns simulation period. These combinatorial drug-design approaches indicate that these three phytochemicals could be developed as potential drugs to treat male infertility.

Approaching the Dimerization Mechanism of Small Molecule Inhibitors Targeting PD-L1 with Molecular Simulation

Inhibitors blocking the PD-1/PD-L1 immune checkpoint demonstrate impressive anti-tumor immunity, and small molecule inhibitors disclosed by the Bristol-Myers Squibb (BMS) company have become a hot topic. In this work, by modifying the carbonyl group of BMS-202 into a hydroxyl group to achieve two enantiomers (MS and MR) with a chiral center, we found that this is an effective way to regulate its hydrophobicity and thus to reduce the negative effect of polar solvation free energy, which enhances the stability of PD-L1 dimer/inhibitor complexes. Moreover, we studied the binding modes of BMS-200 and BMS-202-related small molecule inhibitors by molecular dynamics simulation to explore their inhibitory mechanism targeting PD-L1 dimerization. The results showed that the size exclusion effect of the inhibitors triggered the rearrangement of the residue _ATyr56, leading to the formation of an axisymmetric tunnel-shaped pocket, which is an important structural basis for improving the binding affinity of symmetric inhibitors with PD-L1. Furthermore, after inhibitor dissociation, the conformation of _ATyr123 and _BMet115 rearranged, which blocked the entrance of the binding pocket, while the reverse rearrangements of the same residues occurred when the PD-L1 monomer was complexed with the inhibitors, preparing PD-L1 for dimerization. Overall, this study casts a new light on the inhibitory mechanism of BMS inhibitors targeting PD-L1 dimerization and provides an idea for designing novel small molecule inhibitors for future cancer immunotherapy.

Free energy perturbation-based large-scale virtual screening for effective drug discovery against COVID-19

As a theoretically rigorous and accurate method, FEP-ABFE (Free Energy Perturbation-Absolute Binding Free Energy) calculations showed great potential in drug discovery, but its practical application was difficult due to high computational cost. To rapidly discover antiviral drugs targeting SARS-CoV-2 Mpro and TMPRSS2, we performed FEP-ABFE-based virtual screening for ∼12,000 protein-ligand binding systems on a new generation of Tianhe supercomputer. A task management tool was specifically developed for automating the whole process involving more than 500,000 MD tasks. In further experimental validation, 50 out of 98 tested compounds showed significant inhibitory activity towards Mpro, and one representative inhibitor, dipyridamole, showed remarkable outcomes in subsequent clinical trials. This work not only demonstrates the potential of FEP-ABFE in drug discovery but also provides an excellent starting point for further development of anti-SARS-CoV-2 drugs. Besides, ∼500 TB of data generated in this work will also accelerate the further development of FEP-related methods.

Unveiling Phytoconstituents with Inhibitory Potential Against Tyrosine-Protein Kinase Fyn: A Comprehensive Virtual Screening Approach Targeting Alzheimer's Disease

BACKGROUND

Tyrosine-protein kinase Fyn (Fyn) is a critical signaling molecule involved in various cellular processes, including neuronal development, synaptic plasticity, and disease pathogenesis. Dysregulation of Fyn kinase has been implicated in various complex diseases, including neurodegenerative disorders such as Alzheimer's and Parkinson's diseases, as well as different cancer types. Therefore, identifying small molecule inhibitors that can inhibit Fyn activity holds substantial significance in drug discovery.

OBJECTIVE

The aim of this study was to identify potential small-molecule inhibitors among bioactive phytoconstituents against tyrosine-protein kinase Fyn.

METHODS

Through a comprehensive approach involving molecular docking, drug likeliness filters, and molecular dynamics (MD) simulations, we performed a virtual screening of a natural compounds library. This methodology aimed to pinpoint compounds potentially interacting with Fyn kinase and inhibiting its activity.

RESULTS

This study finds two potential natural compounds: Dehydromillettone and Tanshinone B. These compoundsdemonstrated substantial affinity and specific interactions towards the Fyn binding pocket. Their conformations exhibitedcompatibility and stability, indicating the formation of robust protein-ligand complexes. A significant array of non-covalentinteractions supported the structural integrity of these complexes.

CONCLUSION

Dehydromillettone and Tanshinone B emerge as promising candidates, poised for further optimization as Fynkinase inhibitors with therapeutic applications. In a broader context, this study demonstrates the potential of computationaldrug discovery, underscoring its utility in identifying compounds with clinical significance. The identified inhibitors holdpromise in addressing a spectrum of cancer and neurodegenerative disorders. However, their efficacy and safety necessitatevalidation through subsequent experimental studies.

Virtual screening and structure-based 3D pharmacophore approach to identify small-molecule inhibitors of SARS-CoV-2 Mpro

The outbreak caused by a coronavirus 2 has required quick and potential treatment strategies. The main protease enzyme M^pro plays an important role in the viral replication which renders it an important target for discovering SARS-CoV-2 inhibitors. In this study, 3D pharmacophore structure-based virtual screening and molecular docking were conducted using MOE and Bristol University Docking Engine (BUDE). Around 400,000 molecules of ZINC15 database were docked against the crystal structure of main protease, followed by 3D pharmacophore filtration. Six top-ranked hits (ZINC58717986, ZINC60399606, ZINC58662884, ZINC45988635, ZINC54706757 and ZINC17320595) were identified based on their strong spatial affinity and forming H-bonds with key residues H41, E166, Q189 and T190 of the binding pocket of M^pro SARS-CoV-2. The 6 hits subjected to molecular dynamics simulations for 100 ns followed by binding free energy calculations using MM-PBSA technique. Interestingly, three hits showed free binding energy (ΔG_binding) lower than tert-butyl N-[1-[(2S)-1-[[(2S)-4-(benzylamino)-3,4-dioxo-1-[(3S)-2-oxopyrrolidin-3-yl]butan-2-yl]amino]-3-cyclopropyl-1-oxopropan-2-yl]-2-oxopyridin-3-yl]carbamate (α-ketoamide 13 b) (ΔG_binding) -76.67 ± 0.5 kJ/mol which suggested their potential against SARS-CoV-2. The best binding free energy candidates, ZINC58717986 (ΔG_binding) -98.41 ± 0.7 kJ/mol. The second-best hit candidate, ZINC54706757 (ΔG_binding) -83.4 ± 0.6 kJ/mol and the third one ZINC17320595 (ΔG_binding) -78.85 ± 0.5 kJ/mol. Per residue decomposition free energy indicates H41, S46, H164, E166, D187, Q189 and Q192 are hot spot residues while residues M49, M165, L167 and P168 contribute to the hydrophobic interactions. The pharmacokinetic study suggests that the selected 6 hits possess drug-like properties. The 3D pharmacophore virtual screening, molecular dynamics and MM-PBSA approaches facilitated identification 3 promising hits with low free binding energy as SARS-CoV-2 inhibitors.Communicated by Ramaswamy H. Sarma.

High-Throughput Molecular Dynamics-Based Alchemical Free Energy Calculations for Predicting the Binding Free Energy Change Associated with the Selected Omicron Mutations in the Spike Receptor-Binding Domain of SARS-CoV-2

The ongoing pandemic caused by SARS-CoV-2 has gone through various phases. Since the initial outbreak, the virus has mutated several times, with some lineages showing even stronger infectivity and faster spread than the original virus. Among all the variants, omicron is currently classified as a variant of concern (VOC) by the World Health Organization, as the previously circulating variants have been replaced by it. In this work, we have focused on the mutations observed in omicron sub lineages BA.1, BA.2, BA.4 and BA.5, particularly at the receptor-binding domain (RBD) of the spike protein that is responsible for the interactions with the host ACE2 receptor and binding of antibodies. Studying such mutations is particularly important for understanding the viral infectivity, spread of the disease and for tracking the escape routes of this virus from antibodies. Molecular dynamics (MD) based alchemical free energy calculations have been shown to be very accurate in predicting the free energy change, due to a mutation that could have a deleterious or a stabilizing effect on either the protein itself or its binding affinity to another protein. Here, we investigated the significance of five spike RBD mutations on the stability of the spike protein binding to ACE2 by free energy calculations using high throughput MD simulations. For comparison, we also used conventional MD simulations combined with a Molecular Mechanics-Generalized Born Surface Area (MM-GBSA) based approach, and compared our results with the available experimental data. Overall, the alchemical free energy calculations performed far better than the MM-GBSA approach in predicting the individual impact of the mutations. When considering the experimental variation, the alchemical free energy method was able to produce a relatively accurate prediction for N501Y, the mutant that has previously been reported to increase the binding affinity to hACE2. On the other hand, the other individual mutations seem not to have a significant effect on the spike RBD binding affinity towards hACE2.

Discovery of 2',6-Bis(4-hydroxybenzyl)-2-acetylcyclohexanone, a Novel FtsZ Inhibitor

Multi-drug resistance is increasing in the pathogenic bacterium S. pneumoniae, which is mainly responsible for meningitis and community-acquired pneumonia (CAP), highlighting the need for new anti-pneumococcal agents. We have identified a potential anti-pneumococcal agent, enol 3, which acts by hindering the cell division process by perturbing Z-ring dynamics inside the cell. Enol 3 was also shown to inhibit FtsZ polymerization and induce its aggregation in vitro but does not affect the activity of tubulin and alkaline phosphatase. Docking studies show that 3 binds near the T7 loop, which is the catalytic site of FtsZ. Similar effects on Z-ring and FtsZ assembly were observed in B. subtilis, indicating that 3 could be a broad-spectrum anti-bacterial agent useful in targeting Gram-positive bacteria. In conclusion, compound 3 shows strong anti-pneumococcal activity, prompting further pre-clinical studies to explore its potential.

Multi-Level High-Throughput Screening for Discovery of Ligands That Inhibit Insulin Aggregation

We have developed a multi-level virtual screening protocol to identify lead molecules from the FDA inactives database that can inhibit insulin aggregation. The method is based on the presence of structural and interaction specificity in non-native aggregation pathway protein-protein interactions. Some key challenges specific to the present problem, when compared with native protein association, include structural heterogeneity of the protein species involved, multiple association pathways, and relatively higher probability of conformational rearrangement of the association complex. In this multi-step method, the inactives database was first screened using the dominant pharmacophore features of previously identified molecules shown to significantly inhibit insulin aggregation nucleation by binding to its aggregation-prone conformers. We then performed ensemble docking of several low-energy ligand conformations on these aggregation-prone conformers followed by molecular dynamics simulations and binding affinity calculations on a subset of docked complexes to identify a final set of five potential lead molecules to inhibit insulin aggregation nucleation. Their effect on aggregation inhibition was extensively investigated by incubating insulin under aggregation-prone aqueous buffer conditions (low pH, high temperature). Aggregation kinetics were characterized using size exclusion chromatography and Thioflavin T fluorescence assay, and the secondary structure was determined using circular dichroism spectroscopy. Riboflavin provided the best aggregation inhibition, with 85% native monomer retention after 48 h incubation under aggregation-prone conditions, whereas the no-ligand formulation showed complete monomer loss after 36 h. Further, insulin incubated with two of the screened inactives (aspartame, riboflavin) had the characteristic α-helical dip in CD spectra, while the no-ligand formulation showed a change to β-sheet rich conformations.

Exploring the Potential Role of Theaflavin-3,3′-Digallate in Inhibiting Various Stages of SARS-CoV-2 Life Cycle: An In-Silico Approach

Introduction

Theaflavins belong to the class of polyphenols that are predominantly found in black tea. The major derivatives of theaflavins found in black tea are theaflavin (TF1), theaflavin-3-gallate (TF2A), theaflavin-3′-gallate (TF2B), and theaflavin-3,3′-digallate (TF3). Theaflavin-3,3′-digallate (TF3) is a natural compound present in black tea and known to possess antiviral activity. This study had attempted to explore the potential role of TF3 in inhibiting various stages of the SARS-CoV-2 life cycle.

Methods

Molecular docking studies of TF3 along with positive controls was performed on eight different targets of SARS-CoV-2 followed by binding free energy (MM-GBSA) calculations. The docked complexes with favourable docking and binding free energy results were subjected to molecular dynamics (MD) simulation studies to assess the stability of the dock complex. Finally, TF3 and all the positive controls were taken for ADMET analysis.

Results

The docking and binding free energy results of TF3 was compared against the positive controls. TF3 showed the highest binding energy against all the targets and formed more stable interactions for a longer duration on MD simulations with CLpro, RdRp, helicase and spike protein. Also, the promising in-silico ADMET profile further warrants the exploration of this compound through in-vitro and in-vivo methods.

Conclusion

Through this study, we tried to evaluate the role of theaflavin-3,3’-digallate on multiple targets of SARS-CoV-2, and the positive in-silico results which were obtained on various pharmacodynamic and pharmacokinetic parameters, give a ray of hope as a potential therapeutic drug to this rapidly spreading disease. The search for a curative therapy for SARS-CoV-2 is still ongoing. The favourable preliminary results of TF3 through in-silico analysis offers a ray of hope in ending this devasting pandemic.

Supplementary Information

The online version contains supplementary material available at 10.1007/s42250-022-00376-7.

Mechanism of a Herbal Formula Associated with Prognosis and Immune Infiltration in LIHC: Transcriptomics Analysis and Molecular Dynamics Simulations

Background

The aim of this study is to explore the interactions between effective monomers of herbal formulas and their therapeutic targets using systems biology approaches which may be a promising approach to unraveling their underlying mechanisms. Shentao Ruangan decoction (STRGD), which has been experimentally, clinically demonstrated to be effective in treating liver hepatocellular carcinoma (LIHC), was selected.

Methods

Bioactive ingredients and drug targets of STRGD were retrieved from the traditional Chinese medicine systems pharmacology database and analysis platform and BATMAN-TCM databases. LIHC-related differentially expressed genes (DEGs) and key modules were identified by a weighted gene coexpression network analysis using The Cancer Genome Atlas data. The Kaplan–Meier analysis was used to investigate the relationship between STRGD tumor targets and patients survival. The CIBERSORT deconvolution algorithm was used to analyze the correlation between STRGD tumor targets and infiltrating immune cells. Enrichment analysis was used to analyze biological functions. Interactions between STRGD compounds and LIHC-immune-related genes were investigated using molecular docking and MDS.

Results

We identified 24 STRGD tumor targets, which were found to be correlated with survival and the level of immune cell infiltration in LIHC patients. Immune infiltration, gene set enrichment, and Kyoto Encyclopedia of Genes and Genomes analyses highlighted the roles of T and B cell subsets, which were both related to activator protein 1 (AP1), in STRGD action. Docking studies and HPLC indicated that tanshinone IIA is the main compound of STRGD in LIHC treatment, and MDS showed that the potential LIHC-immune-related targets 1FOS and 1JUN firmly bind to tanshinone IIA.

Conclusions

The mechanisms of STRGD in improving the immune and survival status of LIHC patients include interactions between STRGD compounds and LIHC-immune-related targets. The findings of this study can guide research studies on the potential usefulness of tanshinone IIA in the development of drugs targeting 1JUN and 1FOS for the treatment of LIHC.

Unravelling the Structural Mechanism of Action of 5-methyl-5-[4-(4-oxo-3H-quinazolin-2-yl)phenyl]imidazolidine-2,4-dione in Dual-Targeting Tankyrase 1 and 2: A Novel Avenue in Cancer Therapy

Tankyrase (TNKS) belonging to the poly(ADPribose) polymerase family, are known for their multi-functioning capabilities, and play an essential role in the Wnt β-catenin pathway and various other cellular processes. Although showing inhibitory potential at a nanomolar level, the structural dual-inhibitory mechanism of the novel TNKS inhibitor, 5-methyl-5-[4-(4-oxo-3H-quinazolin-2-yl)phenyl]imidazolidine-2,4-dione, remains unexplored. By employing advanced molecular modeling, this study provides these insights. Results of sequence alignments of binding site residues identified conserved residues; GLY1185 and ILE1224 in TNKS-1 and PHE1035 and PRO1034 in TNKS-2 as crucial mediators of the dual binding mechanism of 5-methyl-5-[4-(4-oxo-3H-quinazolin-2-yl)phenyl]imidazolidine-2,4-dione, corroborated by high per-residue energy contributions and consistent high-affinity interactions of these residues. Estimation of the binding free energy of 5-methyl-5-[4-(4-oxo-3H-quinazolin-2-yl)phenyl]imidazolidine-2,4-dione showed estimated total energy of -43.88 kcal/mol and -30.79 kcal/mol towards TNKS-1 and 2, respectively, indicating favorable analogous dual binding as previously reported. Assessment of the conformational dynamics of TNKS-1 and 2 upon the binding of 5-methyl-5-[4-(4-oxo-3H-quinazolin-2-yl)phenyl]imidazolidine-2,4-dione revealed similar structural changes characterized by increased flexibility and solvent assessible surface area of the residues inferring an analogous structural binding mechanism. Insights from this study show that peculiar, conserved residues are the driving force behind the dual inhibitory mechanism of 5-methyl-5-[4-(4-oxo-3H-quinazolin-2-yl)phenyl]imidazolidine-2,4-dione and could aid in the design of novel dual inhibitors of TNKS-1 and 2 with improved therapeutic properties.

Applying polypharmacology approach for drug repurposing for SARS-CoV2

Exploring the new therapeutic indications of known drugs for treating COVID-19, popularly known as drug repurposing, is emerging as a pragmatic approach especially owing to the mounting pressure to control the pandemic. Targeting multiple targets with a single drug by employing drug repurposing known as the polypharmacology approach may be an optimised strategy for the development of effective therapeutics. In this study, virtual screening has been carried out on seven popular SARS-CoV-2 targets (3CL^pro, PL^pro, RdRp (NSP12), NSP13, NSP14, NSP15, and NSP16). A total of 4015 approved drugs were screened against these targets. Four drugs namely venetoclax, tirilazad, acetyldigitoxin, and ledipasvir have been selected based on the docking score, ability to interact with four or more targets and having a reasonably good number of interactions with key residues in the targets. The MD simulations and MM-PBSA studies showed reasonable stability of protein-drug complexes and sustainability of key interactions between the drugs with their respective targets throughout the course of MD simulations. The identified four drug molecules were also compared with the known drugs namely elbasvir and nafamostat. While the study has provided a detailed account of the chosen protein-drug complexes, it has explored the nature of seven important targets of SARS-CoV-2 by evaluating the protein-drug complexation process in great detail.

In Silico Modeling and Scoring of PROTAC-Mediated Ternary Complex Poses

Proteolysis targeting chimeras (PROTACs) are molecules that induce protein degradation via formation of ternary complexes between an E3 ubiquitin ligase and a target protein. The rational design of PROTACs requires accurate knowledge of the native configuration of the PROTAC-induced ternary complex. This study demonstrates that native and non-native ternary complex poses can be distinguished based on the pose occupancy time in MD, where native poses exhibit longer occupancy times at both room and higher temperatures. Candidate poses are generated by MD sampling and pre-ranked by classic MM/GBSA. A specific heating scheme is then applied to accelerate ternary pose departure, with the pose occupancy time and fraction being measured. This scoring identifies the native pose in all systems tested. Its success is partially attributed to the dynamic nature of pose departure analyses, which accounts for entropic effects typically neglected in the faster static scoring methods, while entropy plays a greater role in protein-protein than in protein-ligand systems.

Structural insights into inhibition of PRRSV Nsp4 revealed by structure-based virtual screening, molecular dynamics, and MM-PBSA studies

BACKGROUND

Porcine reproductive and respiratory syndrome respiratory sickness in weaned and growing pigs, as well as sow reproductive failure, and its infection is regarded as one of the most serious swine illnesses worldwide. Given the current lack of an effective treatment, in this study, we identified natural compounds capable of inhibiting non-structural protein 4 (Nsp4) of the virus, which is involved in their replication and pathogenesis.

RESULTS

We screened natural compounds (n = 97,999) obtained from the ZINC database against Nsp4 and selected the top 10 compounds for analysing protein-ligand interactions and physicochemical properties. The five compounds demonstrating strong binding affinity were then subjected to molecular dynamics simulations (100 ns) and binding free energy calculations. Based on analysis, we identified four possible lead compounds that represent potentially effective drug-like inhibitors.

CONCLUSIONS

These methods identified that these natural compounds are capable of inhibiting Nsp4 and possibly effective as antiviral therapeutics against PRRSV.

Computational Design of Miniproteins as SARS-CoV-2 Therapeutic Inhibitors

A rational therapeutic strategy is urgently needed for combating SARS-CoV-2 infection. Viral infection initiates when the SARS-CoV-2 receptor-binding domain (RBD) binds to the ACE2 receptor, and thus, inhibiting RBD is a promising therapeutic for blocking viral entry. In this study, the structure of lead antiviral candidate binder (LCB1), which has three alpha-helices (H1, H2, and H3), is used as a template to design and simulate several miniprotein RBD inhibitors. LCB1 undergoes two modifications: structural modification by truncation of the H3 to reduce its size, followed by single and double amino acid substitutions to enhance its binding with RBD. We use molecular dynamics (MD) simulations supported by ab initio density functional theory (DFT) calculations. Complete binding profiles of all miniproteins with RBD have been determined. The MD investigations reveal that the H3 truncation results in a small inhibitor with a -1.5 kcal/mol tighter binding to RBD than original LCB1, while the best miniprotein with higher binding affinity involves D17R or E11V + D17R mutation. DFT calculations provide atomic-scale details on the role of hydrogen bonding and partial charge distribution in stabilizing the minibinder:RBD complex. This study provides insights into general principles for designing potential therapeutics for SARS-CoV-2.

Pharmacophore-Based Virtual Screening and Molecular Dynamics Simulation for Identification of a Novel DNA Gyrase B Inhibitor with Benzoxazine Acetamide Scaffold

DNA gyrase B is one of the enzyme targets for antimicrobial drug development, and its absence in mammals makes it a suitable target for the creation of safe antibacterial drugs. We identified six novel hits as DNA gyrase B inhibitors in the present study by employing 3D-pharmacophore structure-based virtual screening. The lead compounds complied with drug-likeness rules and lacked toxicity. Compound 4 (ZINC32858011) showed the highest inhibitory activity with an IC₅₀ value of 6.3 ± 0.1 μM against the DNA gyrase enzyme. In contrast, the positive controls ciprofloxacin and novobiocin used in enzyme inhibition assay had IC₅₀ values of 14.4 ± 0.2 and 12.4 ± 0.2 μM, respectively. The molecular docking of the six hits demonstrated that compounds 1, 2, 4, and 6 had suitable fitting modes inside the binding pocket. Molecular dynamics simulations were carried out for the six hits and the rmsd, rmsf, radius of gyration, and solvent accessible surface area parameters obtained from 100 ns molecular dynamics simulations for the six compounds complexed with a DNA gyrase B protein indicated that compound 4 (ZINC32858011) formed the most stable complex with DNA gyrase B. The binding free energy calculation with the MM-PBSA method suggested that the van der Waals interaction, followed by electrostatic force, played a significant role in the binding. Per-residue free binding energy decomposition showed that Ile78 contributed the most for the binding energy followed by Asn46, Asp49, Glu50, Asp73, Ile78, Pro79, Ala86, Ile90, Val120, Thr165, and Val167.

Discovery of Diverse Natural Products as Inhibitors of SARS-CoV-2 Mpro Protease through Virtual Screening

SARS-CoV-2 is a type of coronavirus responsible for the international outbreak of respiratory illness termed COVID-19 that forced the World Health Organization to declare a pandemic infectious disease situation of international concern at the beginning of 2020. The need for a swift response against COVID-19 prompted to consider different sources to identify bioactive compounds that can be used as therapeutic agents, including available drugs and natural products. Accordingly, this work reports the results of a virtual screening process aimed at identifying antiviral natural product inhibitors of the SARS-CoV-2 M^pro viral protease. For this purpose, ca. 2000 compounds of the Selleck database of Natural Compounds were the subject of an ensemble docking process targeting the M^pro protease. Molecules that showed binding to most of the protein conformations were retained for a further step that involved the computation of the binding free energy of the ligand-M^pro complex along a molecular dynamics trajectory. The compounds that showed a smooth binding free energy behavior were selected for in vitro testing. From the resulting set of compounds, five compounds exhibited an antiviral profile, and they are disclosed in the present work.

Non-carboxylic acid inhibitors of aldose reductase based on N-substituted thiazolidinedione derivatives

In search of dually active PPAR-modulators/aldose reductase (ALR2) inhibitors, 16 benzylidene thiazolidinedione derivatives, previously reported as partial PPARγ agonists, together with additional 18 structural congeners, were studied for aldose reductase inhibitory activity. While no compounds had dual property, our efforts led to the identification of promising inhibitors of ALR2. Eight compounds (11, 15-16, 20-24, 30) from the library of 33 compounds were identified as potent and selective inhibitors of ALR2. Compound 21 was the most effective and selective inhibitor with an IC50 value of 0.95 ± 0.11 and 13.52 ± 0.81 μM against ALR2 and aldehyde reductase (ALR1) enzymes, respectively. Molecular docking and dynamics studies were performed to understand inhibitor-enzyme interactions at the molecular level that determine the potency and selectivity. Compound 21 was further subjected to in silico and in vitro studies to evaluate the pharmacokinetic profile. Being less acidic (pKa = 9.8), the compound might have a superior plasma membrane permeability and reach the cytosolic ALR2. This fact together with excellent drug-likeness criteria points to improved bioavailability compared to the clinically used compound Epalrestat. The designed compounds represent a novel group of non-carboxylate inhibitors of aldose reductase with an improved physicochemical profile.

The role of zinc finger linkers in zinc finger protein binding to DNA

Zinc finger proteins (ZFP) play important roles in cellular processes. The DNA binding region of ZFP consists of 3 zinc finger DNA binding domains connected by amino acid linkers, the sequence TGQKP connects ZF1 and ZF2, and TGEKP connects ZF2 with ZF3. Linkers act to tune the zinc finger protein in the right position to bind its DNA target, the type of amino acid residues and length of linkers reflect on ZF1-ZF2-ZF3 interactions and contribute to the search and recognition process of ZF protein to its DNA target. Linker mutations and the affinity of the resulting mutants to specific and nonspecific DNA targets were studied by MD simulations and MM_GB(PB)SA. The affinity of mutants to DNA varied with type and position of amino acid residue. Mutation of K in TGQKP resulted in loss in affinity due to the loss of positive K interaction with phosphates, mutation of G showed loss in affinity to DNA, WT protein and all linker mutants showed loss in affinity to a nonspecific DNA target, this finding confirms previous reports which interpreted this loss in affinity as due to ZF1 having an anchoring role, and ZF3 playing an explorer role in the binding mechanism. The change in ZFP-DNA affinity with linker mutations is discussed in view of protein structure and role of linker residues in binding.

Search for Novel Lead Inhibitors of Yeast Cytochrome bc1, from Drugbank and COCONUT

In this work we introduce a novel filtering and molecular modeling pipeline based on a fingerprint and descriptor similarity procedure, coupled with molecular docking and molecular dynamics (MD), to select potential novel quoinone outside inhibitors (QoI) of cytochrome bc1 with the aim of determining the same or different chromophores to usual. The study was carried out using the yeast cytochrome bc1 complex with its docked ligand (stigmatellin), using all the fungicides from FRAC code C3 mode of action, 8617 Drugbank compounds and 401,624 COCONUT compounds. The introduced drug repurposing pipeline consists of compound similarity with C3 fungicides and molecular docking (MD) simulations with final QM/MM binding energy determination, while aiming for potential novel chromophores and perserving at least an amide (R1HN(C=O)R2) or ester functional group of almost all up to date C3 fungicides. 3D descriptors used for a similarity test were based on the 280 most stable Padel descriptors. Hit compounds that passed fingerprint and 3D descriptor similarity condition and had either an amide or an ester group were submitted to docking where they further had to satisfy both Chemscore fitness and specific conformation constraints. This rigorous selection resulted in a very limited number of candidates that were forwarded to MD simulations and QM/MM binding affinity estimations by the ORCA DFT program. In this final step, stringent criteria based on (a) sufficiently high frequency of H-bonds; (b) high interaction energy between protein and ligand through the whole MD trajectory; and (c) high enough QM/MM binding energy scores were applied to further filter candidate inhibitors. This elaborate search pipeline led finaly to four Drugbank synthetic lead compounds (DrugBank) and seven natural (COCONUT database) lead compounds—tentative new inhibitors of cytochrome bc1. These eleven lead compounds were additionally validated through a comparison of MM/PBSA free binding energy for new leads against those obtatined for 19 QoIs.

Genetic Algorithms Applied to Thermodynamic Rational Design of Mimetic Antibodies Based on the GB1 Domain of Streptococcal Protein G: An Atomistic Simulation Study

The development of mimetic antibodies (MA) capable of combining the high affinity and selectivity of antibodies with the small size of the peptides has enormous potential for applications in current biotechnology. In this work, we demonstrate that in silico MA design is possible through genetic algorithms (GA) developed from shell scripts capable of combining software commonly used for atomistic simulation. Our results demonstrate that, using the GB1 domain of the streptococcal G protein as a model, it is possible to optimize the molecular recognition capacity of a large MA population in a few generations. In the first case, GA was able to generate 10 MA with binding free energy (BFE) less than the vascular endothelial cell growth factor conjugated with the fms-type tyrosine kinase receptor. In the second case, it generated 13 MA with BFE less than that of the hepatitis C-E2 viral envelope conjugate with the antibody. Through the GA developed in this work, we demonstrate the use of a new protocol, capable of guiding experimental methods for the design of bioactive peptides that can assist in the development of new therapeutic molecules.