Contribution of Conformer Focusing to the Uncertainty in Predicting Free Energies for Protein−Ligand Binding

Computational fragment-based drug design of potential Glo-I inhibitors

In this study, a fragment-based drug design approach, particularly de novo drug design, was implemented utilising three different crystal structures in order to discover new privileged scaffolds against glyoxalase-I enzyme as anticancer agents. The fragments were evoluted to indicate potential inhibitors with high receptor affinities. The resulting compounds were served as a benchmark for choosing similar compounds from the ASINEX® database by applying different computational ligand-based drug design techniques. Afterwards, the selection of potential hits was further aided by various structure-based approaches. Then, 14 compounds were purchased, and tested in vitro against Glo-I enzyme. Of the tested 14 hits, the biological screening results showed humble activities where the percentage of Glo-I inhibition ranged from 0-18.70 %. Compound 19 and compound 28, whose percentage of inhibitions are 18.70 and 15.80%, respectively, can be considered as hits that need further optimisation in order to be converted into lead-like compounds.

Novel PPAR-γ agonists as potential neuroprotective agents against Alzheimer's disease: rational design, synthesis, in silico evaluation, PPAR-γ binding assay and transactivation and expression studies

Alzheimer's disease (AD) is a neurological disorder. It is caused by accumulation of amyloid beta (Aβ) plaques and tau tangles, which gradually leads to cognitive decline and memory loss. Peroxisome proliferator-activated receptor gamma (PPAR-γ), a nuclear receptor, plays a significant role in regulating genes responsible for metabolism and inflammation. Studies have shown that PPAR-γ activation has neuroprotective effects, can potentially reduce inflammation and oxidative stress, and stimulates mitochondrial biogenesis. Current study presents the design, synthesis and in vitro evaluation of PPAR-γ agonists for AD that are tailored to optimize binding with the PPAR-γ receptor. The compounds 4a, 4h and 4j exhibited notable binding affinities towards PPAR-γ LBD, with IC50 values of 8.607, 9.242, and 5.974 μM, respectively, in TR-FRET binding assay. These compounds were cell proliferative and non-cytotoxic in a neuroblastoma cell line (SH-SY5Y). They also demonstrated dose-dependent PPAR-γ activation in transactivation assay. Their neuroprotective effect was studied based on their anti-inflammatory and anti-oxidant potential by reducing the levels of proinflammatory markers (TNF-α, IL-6 and IL-1β) and ROS in Aβ-induced SH-SY5Y neuroblastoma cells using a flow cytometry method. The synthesized compounds also showed interactions in molecular docking study with the PPAR-γ receptor and demonstrated good stability in MD simulation. Our results highlight that through activation of PPAR-γ, the compounds 4a, 4h and 4j offer neuroprotective effects by reducing neuroinflammation and oxidative stress, and hence, they may be considered lead molecules for treating AD.

An ANI-2 enabled open-source protocol to estimate ligand strain after docking

In protein-ligand docking, the score assigned to a protein-ligand complex is approximate. Especially, the internal energy of the ligand is difficult to compute precisely using a molecular mechanics based force-field, introducing significant noise in the rank-ordering of ligands. We propose an open-source protocol (https://github.com/UnixJunkie/MMO), using two quantum mechanics (QM) single point energy calculations, plus a Monte Carlo (Monte Carlo) based ligand minimization procedure in-between, to estimate ligand strain after docking. The MC simulation uses the ANI-2x (QM approximating) force field and is performed in the dihedral space. On some protein targets, using strain filtering after docking allows to significantly improve hit rates. We performed a structure-based virtual screening campaign on nine protein targets from the Laboratoire d'Innovation Thérapeutique-PubChem assays dataset using Cambridge crystallographic data centre genetic optimization for ligand docking. Then, docked ligands were submitted to the strain estimation protocol and the impact on hit rate was analyzed. As for docking, the method does not always work. However, if sufficient active and inactive molecules are known for a given protein target, its efficiency can be evaluated.

Large library docking identifies positive allosteric modulators of the calcium-sensing receptor

Positive allosteric modulator (PAM) drugs enhance the activation of the calcium-sensing receptor (CaSR) and suppress parathyroid hormone (PTH) secretion. Unfortunately, these hyperparathyroidism-treating drugs can induce hypocalcemia and arrhythmias. Seeking improved modulators, we docked libraries of 2.7 million and 1.2 billion molecules against the CaSR structure. The billion-molecule docking found PAMs with a 2.7-fold higher hit rate than the million-molecule library, with hits up to 37-fold more potent. Structure-based optimization led to nanomolar leads. In ex vivo organ assays, one of these PAMs was 100-fold more potent than the standard of care, cinacalcet, and reduced serum PTH levels in mice without the hypocalcemia typical of CaSR drugs. As determined from cryo-electron microscopy structures, the PAMs identified here promote CaSR conformations that more closely resemble the activated state than those induced by the established drugs.

Rationalizing Diverse Binding Mechanisms to the Same Protein Fold: Insights for Ligand Recognition and Biosensor Design

The engineering of novel protein-ligand binding interactions, particularly for complex drug-like molecules, is an unsolved problem, which could enable many practical applications of protein biosensors. In this work, we analyzed two engineered biosensors, derived from the plant hormone sensor PYR1, to recognize either the agrochemical mandipropamid or the synthetic cannabinoid WIN55,212-2. Using a combination of quantitative deep mutational scanning experiments and molecular dynamics simulations, we demonstrated that mutations at common positions can promote protein-ligand shape complementarity and revealed prominent differences in the electrostatic networks needed to complement diverse ligands. MD simulations indicate that both PYR1 protein-ligand complexes bind a single conformer of their target ligand that is close to the lowest free-energy conformer. Computational design using a fixed conformer and rigid body orientation led to new WIN55,212-2 sensors with nanomolar limits of detection. This work reveals mechanisms by which the versatile PYR1 biosensor scaffold can bind diverse ligands. This work also provides computational methods to sample realistic ligand conformers and rigid body alignments that simplify the computational design of biosensors for novel ligands of interest.

The impact of Library Size and Scale of Testing on Virtual Screening

Virtual libraries for ligand discovery have recently increased 10,000-fold, and this is thought to have improved hit rates and potencies from library docking. This idea has not, however, been experimentally tested in direct comparisons of larger-vs-smaller libraries. Meanwhile, though libraries have exploded, the scale of experimental testing has little changed, with often only dozens of high-ranked molecules investigated, making interpretation of hit rates and affinities uncertain. Accordingly, we docked a 1.7 billion molecule virtual library against the model enzyme AmpC β-lactamase, testing 1,521 new molecules and comparing the results to the same screen with a library of 99 million molecules, where only 44 molecules were tested. Encouragingly, the larger screen outperformed the smaller one: hit rates improved by two-fold, more new scaffolds were discovered, and potency improved. Overall, 50-fold more inhibitors were found, supporting the idea that there are many more compounds to be discovered than are being tested. With so many compounds evaluated, we could ask how the results vary with number tested, sampling smaller sets at random from the 1521. Hit rates and affinities were highly variable when we only sampled dozens of molecules, and it was only when we included several hundred molecules that results converged. As docking scores improved, so too did the likelihood of a molecule binding; hit rates improved steadily with docking score, as did affinities. This also appeared true on reanalysis of large-scale results against the σ2 and dopamine D4 receptors. It may be that as the scale of both the virtual libraries and their testing grows, not only are better ligands found but so too does our ability to rank them.

Rationalizing diverse binding mechanisms to the same protein fold: insights for ligand recognition and biosensor design

The engineering of novel protein-ligand binding interactions, particularly for complex drug-like molecules, is an unsolved problem which could enable many practical applications of protein biosensors. In this work, we analyzed two engineer ed biosensors, derived from the plant hormone sensor PYR1, to recognize either the agrochemical mandipropamid or the synthetic cannabinoid WIN55,212-2. Using a combination of quantitative deep mutational scanning experiments and molecular dynamics simulations, we demonstrated that mutations at common positions can promote protein-ligand shape complementarity and revealed prominent differences in the electrostatic networks needed to complement diverse ligands. MD simulations indicate that both PYR1 protein-ligand complexes bind a single conformer of their target ligand that is close to the lowest free energy conformer. Computational design using a fixed conformer and rigid body orientation led to new WIN55,212-2 sensors with nanomolar limits of detection. This work reveals mechanisms by which the versatile PYR1 biosensor scaffold can bind diverse ligands. This work also provides computational methods to sample realistic ligand conformers and rigid body alignments that simplify the computational design of biosensors for novel ligands of interest.

Deep computational phenotyping of genomic variants impacting the SET domain of KMT2C reveal molecular mechanisms for their dysfunction

Introduction: Kleefstra Syndrome type 2 (KLEFS-2) is a genetic, neurodevelopmental disorder characterized by intellectual disability, infantile hypotonia, severe expressive language delay, and characteristic facial appearance, with a spectrum of other distinct clinical manifestations. Pathogenic mutations in the epigenetic modifier type 2 lysine methyltransferase KMT2C have been identified to be causative in KLEFS-2 individuals. Methods: This work reports a translational genomic study that applies a multidimensional computational approach for deep variant phenotyping, combining conventional genomic analyses, advanced protein bioinformatics, computational biophysics, biochemistry, and biostatistics-based modeling. We use standard variant annotation, paralog annotation analyses, molecular mechanics, and molecular dynamics simulations to evaluate damaging scores and provide potential mechanisms underlying KMT2C variant dysfunction. Results: We integrated data derived from the structure and dynamics of KMT2C to classify variants into SV (Structural Variant), DV (Dynamic Variant), SDV (Structural and Dynamic Variant), and VUS (Variant of Uncertain Significance). When compared with controls, these variants show values reflecting alterations in molecular fitness in both structure and dynamics. Discussion: We demonstrate that our 3D models for KMT2C variants suggest distinct mechanisms that lead to their imbalance and are not predictable from sequence alone. Thus, the missense variants studied here cause destabilizing effects on KMT2C function by different biophysical and biochemical mechanisms which we adeptly describe. This new knowledge extends our understanding of how variations in the KMT2C gene cause the dysfunction of its methyltransferase enzyme product, thereby bearing significant biomedical relevance for carriers of KLEFS2-associated genomic mutations.

Terpenoid phytocompounds from mangrove plant Xylocarpus moluccensis as possible inhibitors against SARS-CoV-2: In silico strategy

COVID-19 shook the world during the pandemic, where the climax it reached was vaccine manufacturing at an unfathomable pace. Alternative promising solutions to prevent infection from SARS-CoV-2 and its variants will remain crucial in the years to come. Due to its key role in viral replication, the major protease (Mpro) enzyme of SARS-CoV-2 can be an attractive therapeutic target. In the present work, natural terpenoids from mangrove medicinal plant Xylocarpus moluccensis (Lam.) M. Roem. were screened using computational methods for inhibition of Mpro protein. Out of sixty-seven terpenoids, Angolensic acid methyl ester, Moluccensin V, Thaixylomolin F, Godavarin J, and Xylomexicanolide A were shortlisted based on their docking scores and interaction affinities (- 13.502 to - 15.52 kcal/mol). The efficacy was validated by the 100 ns molecular dynamics study. Lead terpenoids were within the acceptable range of RMSD and RMSF with a mean value of 2.5 Å and 1.5 Å, respectively indicating that they bound tightly within Mpro and there was minimal fluctuation and stability of Mpro upon binding of these terpenoids. The utmost favorable binding strengths as calculated by MM-GBSA, were of Angolensic acid methyl ester and Moluccensin V with binding free energies (ΔGbind) of - 39.084, and - 43.160 kcal/mol, respectively. The terpenoids showed no violations in terms of Drug Likeliness and ADMET predictions. Overall, the findings indicate that Angolensic acid methyl ester and Moluccensin V are effective terpenoids having strong binding interaction with Mpro protein, which must be tested in vitro as an effective anti-SARS-CoV-2 drug.

Novel Derivatives of Eugenol as a New Class of PPARγ Agonists in Treating Inflammation: Design, Synthesis, SAR Analysis and In Vitro Anti-Inflammatory Activity

The main objective of this research was to develop novel compounds from readily accessed natural products especially eugenol with potential biological activity. Eugenol, the principal chemical constituent of clove (Eugenia caryophyllata) from the family Myrtaceae is renowned for its pharmacological properties, which include analgesic, antidiabetic, antioxidant, anticancer, and anti-inflammatory effects. According to reports, PPARγ regulates inflammatory reactions. The synthesized compounds were structurally analyzed using FT-IR, ¹HNMR, ¹³CNMR, and mass spectroscopy techniques. Molecular docking was performed to analyze binding free energy and important amino acids involved in the interaction between synthesized derivatives and the target protein. The development of the structure-activity relationship is based on computational studies. Additionally, the stability of the best-docked protein-ligand complexes was assessed using molecular dynamic modeling. The in-vitro PPARγ competitive binding Lanthascreen TR-FRET assay was used to confirm the affinity of compounds to the target protein. All the synthesized derivatives were evaluated for an in vitro anti-inflammatory activity using an albumin denaturation assay and HRBC membrane stabilization at varying concentrations from 6.25 to 400 µM. In this background, with the aid of computational research, we were able to design six novel derivatives of eugenol synthesized, analyzed, and utilized TR-FRET competitive binding assay to screen them for their ability to bind PPARγ. Anti-inflammatory activity evaluation through in vitro albumin denaturation and HRBC method revealed that 1f exhibits maximum inhibition of heat-induced albumin denaturation at 50% and 85% protection against HRBC lysis at 200 and 400 µM, respectively. Overall, we found novel derivatives of eugenol that could potentially reduce inflammation by PPARγ agonism.

Novel Thiosemicarbazone Quantum Dots in the Treatment of Alzheimer's Disease Combining In Silico Models Using Fingerprints and Physicochemical Descriptors

Searching for thiosemicarbazone derivatives with the potential to inhibit acetylcholinesterase for the treatment of Alzheimer's disease (AD) is an important current goal. The QSAR_KPLS, QSAR_ANN, and QSAR_SVR models were constructed using binary fingerprints and physicochemical (PC) descriptors of 129 thiosemicarbazone compounds screened from a database of 3791 derivatives. The R ² and Q ² values for the QSAR_KPLS, QSAR_ANN, and QSAR_SVR models are greater than 0.925 and 0.713 using dendritic fingerprint (DF) and PC descriptors, respectively. The in vitro pIC₅₀ activities of four new design-oriented compounds N1, N2, N3, and N4, from the QSAR_KPLS model using DFs, are consistent with the experimental results and those from the QSAR_ANN and QSAR_SVR models. The designed compounds N1, N2, N3, and N4 do not violate Lipinski-5 and Veber rules using the ADME and BoiLED-Egg methods. The binding energy, kcal mol^-1, of the novel compounds to the 1ACJ-PDB protein receptor of the AChE enzyme was also obtained by molecular docking and dynamics simulations consistent with those predicted from the QSAR_ANN and QSAR_SVR models. New compounds N1, N2, N3, and N4 were synthesized, and the experimental in vitro pIC₅₀ activity was determined in agreement with those obtained from in silico models. The newly synthesized thiosemicarbazones N1, N2, N3, and N4 can inhibit 1ACJ-PDB, which is predicted to be able to cross the barrier. The DFT B3LYP/def-SV(P)-ECP quantization calculation method was used to calculate E _HOMO and E _LUMO to account for the activities of compounds N1, N2, N3, and N4. The quantum calculation results explained are consistent with those obtained in in silico models. The successful results here may contribute to the search for new drugs for the treatment of AD.

New insights into the neuroprotective and beta-secretase1 inhibitor profiles of tirandamycin B isolated from a newly found Streptomyces composti sp. nov

Tirandamycin (TAM B) is a tetramic acid antibiotic discovered to be active on a screen designed to find compounds with neuroprotective activity. The producing strain, SBST2-5^T, is an actinobacterium that was isolated from wastewater treatment bio-sludge compost collected from Suphanburi province, Thailand. Taxonomic characterization based on a polyphasic approach indicates that strain SBST2-5^T is a member of the genus Streptomyces and shows low average nucleotide identity (ANI) (81.7%), average amino-acid identity (AAI) (78.5%), and digital DNA-DNA hybridization (dDDH) (25.9%) values to its closest relative, Streptomyces thermoviolaceus NBRC 13905^T, values that are significantly below the suggested cut-off values for the species delineation, indicating that strain SBST2-5^T could be considered to represent a novel species of the genus Streptomyces. The analysis of secondary metabolites biosynthetic gene clusters (smBGCs) in its genome and chemical investigation led to the isolation of TAM B. Interestingly, TAM B at 20 µg/mL displayed a suppressive effect on beta-secretase 1 (BACE1) with 68.69 ± 8.84% inhibition. Molecular docking simulation reveals the interaction mechanism between TAM B and BACE1 that TAM B was buried in the pocket of BACE-1 by interacting with amino acids Thr231, Asp 228, Gln73, Lys 107 via hydrogen bond and Leu30, Tyr71, Phe108, Ile118 via hydrophobic interaction, indicating that TAM B represents a potential active BACE1 inhibitor. Moreover, TAM B can protect the neuron cells significantly (% neuron viability = 83.10 ± 9.83% and 112.72 ± 6.83%) from oxidative stress induced by serum deprivation and Aβ_1-42 administration models at 1 ng/mL, respectively, without neurotoxicity on murine P19-derived neuron cells nor cytotoxicity against Vero cells. This study was reportedly the first study to show the neuroprotective and BACE1 inhibitory activities of TAM B.

Discovery of new PKN2 inhibitory chemotypes via QSAR-guided selection of docking-based pharmacophores

Serine/threonine-protein kinase N2 (PKN2) plays an important role in cell cycle progression, cell migration, cell adhesion and transcription activation signaling processes. In cancer, however, it plays important roles in tumor cell migration, invasion and apoptosis. PKN2 inhibitors have been shown to be promising in treating cancer. This prompted us to model this interesting target using our QSAR-guided selection of docking-based pharmacophores approach where numerous pharmacophores are extracted from docked ligand poses and allowed to compete within the context of QSAR. The optimal pharmacophore was sterically-refined, validated by receiver operating characteristic (ROC) curve analysis and used as virtual search query to screen the National Cancer Institute (NCI) database for new promising anti-PKN2 leads of novel chemotypes. Three low micromolar hits were identified with IC₅₀ values ranging between 9.9 and 18.6 µM. Pharmacological assays showed promising cytotoxic properties for active hits in MTT and wound healing assays against MCF-7 and PANC-1 cancer cells.

Identification of novel inhibitors of S-adenosyl-L-homocysteine hydrolase via structure-based virtual screening and molecular dynamics simulations

S-adenosyl-L-homocysteine hydrolase (SAHase) is an important regulator in the methylation reactions in many organisms and thus is crucial for numerous cellular functions. In recent years, SAHase has become one of the popular targets for drug design, and SAHase inhibitors have exhibited potent antiviral activity. In this study, we established the complex-based pharmacophore models based on the known crystal complex of SAHase (PDB ID: 1A7A) to screen the drug-likeness compounds of ChEMBL database. Then, three molecular docking programs were used to validate the reliability of compounds, involving Libdock, CDOCKER, and AutoDock Vina programs. The four promising hit compounds (CHEMBL420751, CHEMBL346387, CHEMBL1569958, and CHEMBL4206648) were performed molecular dynamics simulations and MM-PBSA calculations to evaluate their stability and binding-free energy in the binding site of SAHase. After screening and analyzing, the hit compounds CHEMBL420751 and CHEMBL346387 were suggested to further research to obtain novel potential SAHase inhibitors. A series of computer-aided drug design methods, including pharmacophore, molecular docking, molecular dynamics simulation and MM-PBSA calculations, were employed in this study to identity novel inhibitors of S-adenosyl-L-homocysteine hydrolase (SAHase). Some compounds from virtual screening could form various interactions with key residues of SAHase. Among them, compounds CHEMBL346387 and CHEMBL420751 exhibited potent binding affinity from molecular docking and MM-PBSA, and maintained good stability at the binding site during molecular dynamics simulations as well. All these results indicated that the selected compounds might have the potential to be novel SAHase inhibitors.

Pharmacophore Modeling of Targets Infested with Activity Cliffs via Molecular Dynamics Simulation Coupled with QSAR and Comparison with other Pharmacophore Generation Methods: KDR as Case Study

Activity cliffs (ACs) are defined as pairs of structurally similar compounds with large difference in their potencies against certain biotarget. We recently proposed that potent AC members induce significant entropically-driven conformational modifications of the target that unveil additional binding interactions, while their weakly-potent counterparts are enthalpically-driven binders with little influence on the protein target. We herein propose to extract pharmacophores for ACs-infested target(s) from molecular dynamics (MD) frames of purely "enthalpic" potent binder(s) complexed within the particular target. Genetic function algorithm/machine learning (GFA/ML) can then be employed to search for the best possible combination of MD pharmacophore(s) capable of explaining bioactivity variations within a list of inhibitors. We compared the performance of this approach with established ligand-based and structure-based methods. Kinase inserts domain receptor (KDR) was used as a case study. KDR plays a crucial role in angiogenic signaling and its inhibitors have been approved in cancer treatment. Interestingly, GFA/ML selected, MD-based, pharmacophores were of comparable performances to ligand-based and structure-based pharmacophores. The resulting pharmacophores and QSAR models were used to capture hits from the national cancer institute list of compounds. The most active hit showed anti-KDR IC50 of 2.76 µM.

A Functional Screening Identifies a New Organic Selenium Compound Targeting Cancer Stem Cells: Role of c-Myc Transcription Activity Inhibition in Liver Cancer

Cancer stem cells (CSCs) are reported to play essential roles in chemoresistance and metastasis. Pathways regulating CSC self-renewal and proliferation, such as Hedgehog, Notch, Wnt/β-catenin, TGF-β, and Myc, may be potential therapeutic targets. Here, a functional screening from the focused library with 365 compounds is performed by a step-by-step strategy. Among these candidate molecules, phenyl-2-pyrimidinyl ketone 4-allyl-3-amino selenourea (CU27) is chosen for further identification because it proves to be the most effective compound over others on CSC inhibition. Through ingenuity pathway analysis, it is shown CU27 may inhibit CSC through a well-known stemness-related transcription factor c-Myc. Gene set enrichment analysis, dual-luciferase reporter assays, expression levels of typical c-Myc targets, molecular docking, surface plasmon resonance, immunoprecipitation, and chromatin immunoprecipitation are conducted. These results together suggest CU27 binds c-Myc bHLH/LZ domains, inhibits c-Myc-Max complex formation, and prevents its occupancy on target gene promoters. In mouse models, CU27 significantly sensitizes sorafenib-resistant tumor to sorafenib, reduces the primary tumor size, and inhibits CSC generation, showing a dramatic anti-metastasis potential. Taken together, CU27 exerts inhibitory effects on CSC and CSC-associated traits in hepatocellular carcinoma (HCC) via c-Myc transcription activity inhibition. CU27 may be a promising therapeutic to treat sorafenib-resistant HCC.

A new approach used in docking study for predicting the combination drug efficacy in EML4-ALK target of NSCLC

Combination drug treatments are usually used in many diseases, including cancers and AIDS. This treatment strategy is known as one of the cornerstone in therapies, which potentially reduces drug toxicity and drug resistance and also enhances therapeutic efficacy. Before using a drug in treatment, several experimental studies are done in vivo and in vitro to ensure the drug's efficacy. In such experimental studies, the drug's efficacy is evaluated with the help of drug dose ratio. In the combination drug experimental studies, the efficacy of the drugs is quantified with the Combination Index (CI) value and then interpreted by various terminologies like synergy, additive, and antagonism. Several computational models have now been invented for the speedy identification of combination drug efficacy. Unfortunately, none of these models have predicted the atomic level interaction of the combination drug with the target protein. This type of intermolecular interaction can be identified with the help of docking software. In the proposed work, we try to identify the intermolecular interaction and efficacy of the combination drug Crzizotinib and Temozolomide in the target of EML4-ALK in NSCLC by in silico study. The result of the study was evaluated with drug properties and Complex Energy (CE) of the docked complex rather than using docking score and binding energy. From this study, we could understand that first, Crizotinib and then after the Temozolomide drug binded on the EML4-ALK protein complex, showed very least CE and also identified that the combination of Crizotinib and Temozolomide drug are more effective in NSCLC.Communicated by Ramaswamy H. Sarma.

Novel derivatives of eugenol as potent anti-inflammatory agents via PPARγ agonism: rational design, synthesis, analysis, PPARγ protein binding assay and computational studies

Eugenol is a natural product abundantly found in clove buds known for its pharmacological activities such as anti-inflammatory, antidiabetic, antioxidant, and anticancer activities. It is well known from the literature that peroxisome proliferator-activated receptors (PPARγ) have been reported to regulate inflammatory responses. In this backdrop, we rationally designed semi-synthetic derivatives of eugenol with the aid of computational studies, and synthesized, purified, and analyzed four eugenol derivatives as PPARγ agonists. Compounds were screened for PPARγ protein binding by time-resolved fluorescence (TR-FRET) assay. The biochemical assay results were favorable for 1C which exhibited significant binding affinity with an IC50 value of 10.65 μM as compared to the standard pioglitazone with an IC50 value of 1.052 μM. In addition to the protein binding studies, as a functional assay, the synthesized eugenol derivatives were screened for in vitro anti-inflammatory activity at concentrations ranging from 6.25 μM to 400 μM. Among the four compounds tested 1C shows reasonably good anti-inflammatory activity with an IC50 value of 133.8 μM compared to a standard diclofenac sodium IC50 value of 54.32 μM. Structure-activity relationships are derived based on computational studies. Additionally, molecular dynamics simulations were performed to examine the stability of the protein-ligand complex, the dynamic behavior, and the binding affinity of newly synthesized molecules. Altogether, we identified novel eugenol derivatives as potential anti-inflammatory agents via PPARγ agonism.

Engineering and screening of novel β-1,3-xylanases with desired hydrolysate type by optimized ancestor sequence reconstruction and data mining

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A novel integrative strategy for engineering β-1,3-xylanases with desired products.

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AncXyl10 is the first successful example of ASR to shift the hydrolysate types.

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The hydrolysates of AncXyl10 was only β-1,3-xylobiose and β-1,3-xylotriose.

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The underlying mechanism laid a new groundwork towards hydrolase engineering.

Engineering of hydrolases to shift their hydrolysate types has not been attempted so far, though computer-assisted enzyme design has been successful. A novel integrative strategy for engineering and screening the β-1,3-xylanase with desired hydrolysate types was proposed, with the purpose to solve problems that the separation and preparation of β-1,3-xylo-oligosaccharides was in high cost yet in low yield as monosaccharides existed in the hydrolysates. By classifying the hydrolysate types and coding them into numerical values, two robust mathematical models with five selected attributes from molecular docking were established based on LogitBoost and partial least squares regression with overall accuracy of 83.3% and 100%, respectively. Then, they were adopted for efficient screening the potential mutagenesis library of β-1,3-xylanases that only product oligosaccharides. The virtually designed AncXyl10 was selected and experimentally verified to produce only β-1,3-xylobiose (60.38%) and β-1,3-xylotriose (39.62%), which facilitated the preparation of oligosaccharides with high purity. The underlying mechanism of AncXyl10 may associated with the gap processing and ancestral amino acid substitution in the process of ancestral sequence reconstruction. Since many carbohydrate-active enzymes have highly conserved active sites, the strategy and their biomolecular basis will shield a new light for engineering carbohydrates hydrolase to produce specific oligosaccharides.

Development of an Automatic Pipeline for Participation in the CELPP Challenge

The prediction of how a ligand binds to its target is an essential step for Structure-Based Drug Design (SBDD) methods. Molecular docking is a standard tool to predict the binding mode of a ligand to its macromolecular receptor and to quantify their mutual complementarity, with multiple applications in drug design. However, docking programs do not always find correct solutions, either because they are not sampled or due to inaccuracies in the scoring functions. Quantifying the docking performance in real scenarios is essential to understanding their limitations, managing expectations and guiding future developments. Here, we present a fully automated pipeline for pose prediction validated by participating in the Continuous Evaluation of Ligand Pose Prediction (CELPP) Challenge. Acknowledging the intrinsic limitations of the docking method, we devised a strategy to automatically mine and exploit pre-existing data, defining—whenever possible—empirical restraints to guide the docking process. We prove that the pipeline is able to generate predictions for most of the proposed targets as well as obtain poses with low RMSD values when compared to the crystal structure. All things considered, our pipeline highlights some major challenges in the automatic prediction of protein–ligand complexes, which will be addressed in future versions of the pipeline.

Improving Catalytic Activity and Thermal Stability of Methyl-Parathion Hydrolase for Degrading the Pesticide of Methyl-Parathion

Pesticides are indispensable in today’s agriculture. Methyl-parathion hydrolase (MPH, E.C.3.1.8.1) could hydrolyze organophosphorus pesticides, including methyl-parathion. MPH could rehabilitate soil and water resources contaminated by organophosphorus pesticides. However, natural MPHs generally exhibited a low tolerance to high temperatures and low catalytic efficiency. In this study, we improved the catalytic efficiency toward methyl-parathion and the thermal stability of the MPH from Pseudomonas sp. WBC-3 through saturation mutagenesis and fusion with self-assembling amphipathic peptides (SAP). The experimental characterization showed that compared to the wild-type enzyme, the kcat/Km of the mutant T271S yielded by saturation mutagenesis was increased by 224.3% compared to the wild-type MPH. T50 and Tm of SAP3-MPH with an SAP fused at the N-terminus were increased by 6.2°C and 6.0°C, respectively. Compared to the wild-type enzyme, T271S fused with SAP3 at the N-terminus (SAP3-T271S) exhibited a 2.1-fold increase in kcat/Km without significantly affecting the thermal stability. The simultaneous improvement of the catalytic efficiency and thermal stability of MPH would be beneficial for its application in the degradation and detection of organophosphorus pesticides. Furthermore, our study provides a potential combination strategy for the design of the other enzyme preparations of pollutant degradation.

Pharmacophore Based Design of Probable FGFR-1 Inhibitors from the 3D Crystal Structure of Infigratinib - A Drug Used in the Treatment of Cholangiocarcinomas

Background:

Pemigatinib (INCB054828) and Infigratinib (BGJ398) are the few selective drugs that are approved by the FDA to treat cholangiocarcinoma, a rare form of bile duct cancer. Infigratinib is a pan FGFR inhibitor and has been found promising in Phase-3, first-line PROOF clinical trial. So, screening drug-like compounds having similar pharmacophoric features like infigratinib is the inspiration of the present work.

Objective:

The objective was to identify drug-like compounds with similar pharmacophoric features as in infigratinib. The compounds screened through the 3D query pharmacophore of infigratinib were also predicted for ADMET properties so that the compounds may have good bioavailability.

Method:

A pharmacophore was generated from the crystal structure of infigratinib with several pharmacophoric features such as hydrogen bond donor, hydrophobic, positive ionizable, and ring aromatic. MayBridge database containing 65,263 compounds was used for virtual screening (VS) using LibDock. The initial Hit compounds were subjected to ADMET predictions. Finally, two Hit compounds were selected and docked with the FGFR-1 receptor to predict the interaction of the ligand atoms with the amino acid residues of the receptor's active site.

Result:

The fit score for infigratinib, N-(4-fluorophenyl)-2-(5-((2-(4-methoxy-2,5-dimethylphenyl)-2- oxoethyl)thio)-4-methyl-4H-1,2,4-triazol-3-yl)acetamide (Hit-1) and 4-(4-((2-(5,6-dimethyl-1H-benzo[d] imidazol-2-yl)ethyl)carbamoyl)pyridin-2-yl)-1-methylpiperazin-1-ium (Hit-4) is 4.58901, 4.36649, and 3.71732, respectively. The LibDock score of infigratinib, Hit-1, and Hit-4 is 122.474, 123.289, and 123.353, respectively. The binding affinity score (-PLP1) of infigratinib, Hit-1, and Hit-4 is -143.19, - 102.72, and -91.71.

Conclusion:

The present study concluded that the two compounds designated as Hit-1 and Hit-4 have been identified as binders of FGFR-1, and Hit-4 occupies the whole pharmacophoric space of infigratinib, and both the compounds LibDock scores are better than the infigratinib. The two compounds Hit-1 and Hit-4 may be synthesized and studied for their enzyme inhibition assay on FGFR-1 and biologically evaluated on different cell lines for Cholangiocarcinoma.

Structure-Based Virtual Screening for Ligands of G Protein-Coupled Receptors: What Can Molecular Docking Do for You?

G protein-coupled receptors (GPCRs) constitute the largest family of membrane proteins in the human genome and are important therapeutic targets. During the last decade, the number of atomic-resolution structures of GPCRs has increased rapidly, providing insights into drug binding at the molecular level. These breakthroughs have created excitement regarding the potential of using structural information in ligand design and initiated a new era of rational drug discovery for GPCRs. The molecular docking method is now widely applied to model the three-dimensional structures of GPCR-ligand complexes and screen for chemical probes in large compound libraries. In this review article, we first summarize the current structural coverage of the GPCR superfamily and the understanding of receptor-ligand interactions at atomic resolution. We then present the general workflow of structure-based virtual screening and strategies to discover GPCR ligands in chemical libraries. We assess the state of the art of this research field by summarizing prospective applications of virtual screening based on experimental structures. Strategies to identify compounds with specific efficacy and selectivity profiles are discussed, illustrating the opportunities and limitations of the molecular docking method. Our overview shows that structure-based virtual screening can discover novel leads and will be essential in pursuing the next generation of GPCR drugs. SIGNIFICANCE STATEMENT: Extraordinary advances in the structural biology of G protein-coupled receptors have revealed the molecular details of ligand recognition by this large family of therapeutic targets, providing novel avenues for rational drug design. Structure-based docking is an efficient computational approach to identify novel chemical probes from large compound libraries, which has the potential to accelerate the development of drug candidates.

The reorganization energy of compounds upon binding to proteins, from dynamic and solvated bound and unbound states

The intramolecular reorganization energy (ΔE_Reorg) of compounds upon binding to proteins is a component of the binding free energy, which has long received particular attention, for fundamental and practical reasons. Understanding ΔE_Reorg would benefit the science of molecular recognition and drug design. For instance, the tolerable strain energy of compounds upon binding has been elusive. Prior studies found some large ΔE_Reorg values (e.g. > 10 kcal/mol), received with skepticism since they imply excessive opposition to binding. Indeed, estimating ΔE_Reorg is technically difficult. Typically, ΔE_Reorg has been approached by taking two energy-minimized conformers representing the bound and unbound states, and subtracting their conformational energy. This is a drastic oversimplification, liable to conformational collapse of the unbound conformer. Instead, the present work applies extensive molecular dynamics (MD) and the modern OPLS3 force-field to simulate compounds bound and unbound states, in explicit solvent under physically relevant conditions. The thermalized unbound compounds populate multiple conformations, not reducible to one or a few energy-minimized conformers. The intramolecular energies in the bound and unbound states were averaged over pertinent conformational ensembles, and the reorganization enthalpy upon binding (ΔH_Reorg) deduced by subtraction. This was applied to 76 systems, including 43 approved drugs, carefully selected for i) the quality of the bioactive X-ray structures and ii) the diversity of the chemotypes, their properties and protein targets. It yielded comparatively low ΔH_Reorg values (median = 1.4 kcal/mol, mean = 3.0 kcal/mol). A new finding is the observation of negative ΔH_Reorg values. Indeed, reorganization energies do not have to oppose binding, e.g. when intramolecular interactions stabilize preferentially the bound state. Conversely, even with competing water molecules, intramolecular interactions can occur predominantly in the unbound compound, and be replaced by intermolecular counterparts upon protein binding. Such disruption of intramolecular interactions upon binding gives rise to occasional larger ΔH_Reorg values. Such counterintuitive larger ΔH_Reorg values may be rationalized as a redistribution of interactions upon binding, qualitatively compatible with binding.

Nonantimicrobial Actions of Macrolides: Overview and Perspectives for Future Development

Macrolides are among the most widely prescribed broad spectrum antibacterials, particularly for respiratory infections. It is now recognized that these drugs, in particular azithromycin, also exert time-dependent immunomodulatory actions that contribute to their therapeutic benefit in both infectious and other chronic inflammatory diseases. Their increased chronic use in airway inflammation and, more recently, of azithromycin in COVID-19, however, has led to a rise in bacterial resistance. An additional crucial aspect of chronic airway inflammation, such as chronic obstructive pulmonary disease, as well as other inflammatory disorders, is the loss of epithelial barrier protection against pathogens and pollutants. In recent years, azithromycin has been shown with time to enhance the barrier properties of airway epithelial cells, an action that makes an important contribution to its therapeutic efficacy. In this article, we review the background and evidence for various immunomodulatory and time-dependent actions of macrolides on inflammatory processes and on the epithelium and highlight novel nonantibacterial macrolides that are being studied for immunomodulatory and barrier-strengthening properties to circumvent the risk of bacterial resistance that occurs with macrolide antibacterials. We also briefly review the clinical effects of macrolides in respiratory and other inflammatory diseases associated with epithelial injury and propose that the beneficial epithelial effects of nonantibacterial azithromycin derivatives in chronic inflammation, even given prophylactically, are likely to gain increasing attention in the future. SIGNIFICANCE STATEMENT: Based on its immunomodulatory properties and ability to enhance the protective role of the lung epithelium against pathogens, azithromycin has proven superior to other macrolides in treating chronic respiratory inflammation. A nonantibiotic azithromycin derivative is likely to offer prophylactic benefits against inflammation and epithelial damage of differing causes while preserving the use of macrolides as antibiotics.

A practical guide to large-scale docking

Structure-based docking screens of large compound libraries have become common in early drug and probe discovery. As computer efficiency has improved and compound libraries have grown, the ability to screen hundreds of millions, and even billions, of compounds has become feasible for modest-sized computer clusters. This allows the rapid and cost-effective exploration and categorization of vast chemical space into a subset enriched with potential hits for a given target. To accomplish this goal at speed, approximations are used that result in undersampling of possible configurations and inaccurate predictions of absolute binding energies. Accordingly, it is important to establish controls, as are common in other fields, to enhance the likelihood of success in spite of these challenges. Here we outline best practices and control docking calculations that help evaluate docking parameters for a given target prior to undertaking a large-scale prospective screen, with exemplification in one particular target, the melatonin receptor, where following this procedure led to direct docking hits with activities in the subnanomolar range. Additional controls are suggested to ensure specific activity for experimentally validated hit compounds. These guidelines should be useful regardless of the docking software used. Docking software described in the outlined protocol (DOCK3.7) is made freely available for academic research to explore new hits for a range of targets.

Ligand Strain Energy in Large Library Docking

While small molecule internal strain is crucial to molecular docking, using it in evaluating ligand scores has remained elusive. Here, we investigate a technique that calculates strain using relative torsional populations in the Cambridge Structural Database, enabling fast precalculation of these energies. In retrospective studies of large docking screens of the dopamine D4 receptor and of AmpC β-lactamase, where close to 600 docking hits were tested experimentally, including such strain energies improved hit rates by preferentially reducing the ranks of strained high-scoring decoy molecules. In a 40-target subset of the DUD-E benchmark, we found two thresholds that usefully distinguished between ligands and decoys: one based on the total strain energy of the small molecules and another based on the maximum strain allowed for any given torsion within them. Using these criteria, about 75% of the benchmark targets had improved enrichment after strain filtering. Relying on precalculated population distributions, this approach is rapid, taking less than 0.04 s to evaluate a conformation on a standard core, making it pragmatic for precalculating strain in even ultralarge libraries. Since it is scoring function agnostic, it may be useful to multiple docking approaches; it is openly available at http://tldr.docking.org.

Systematic comparison of ligand-based and structure-based virtual screening methods on poly (ADP-ribose) polymerase-1 inhibitors

The poly (ADP-ribose) polymerase-1 (PARP1) has been regarded as a vital target in recent years and PARP1 inhibitors can be used for ovarian and breast cancer therapies. However, it has been realized that most of PARP1 inhibitors have disadvantages of low solubility and permeability. Therefore, by discovering more molecules with novel frameworks, it would have greater opportunities to apply it into broader clinical fields and have a more profound significance. In the present study, multiple virtual screening (VS) methods had been employed to evaluate the screening efficiency of ligand-based, structure-based and data fusion methods on PARP1 target. The VS methods include 2D similarity screening, structure-activity relationship (SAR) models, docking and complex-based pharmacophore screening. Moreover, the sum rank, sum score and reciprocal rank were also adopted for data fusion methods. The evaluation results show that the similarity searching based on Torsion fingerprint, six SAR models, Glide docking and pharmacophore screening using Phase have excellent screening performance. The best data fusion method is the reciprocal rank, but the sum score also performs well in framework enrichment. In general, the ligand-based VS methods show better performance on PARP1 inhibitor screening. These findings confirmed that adding ligand-based methods to the early screening stage will greatly improve the screening efficiency, and be able to enrich more highly active PARP1 inhibitors with diverse structures.

High-Throughput Docking and Molecular Dynamics Simulations towards the Identification of Potential Inhibitors against Human Coagulation Factor XIIa

Human coagulation factor XIIa (FXIIa) is a trypsin-like serine protease that is involved in pathologic thrombosis. As a potential target for designing safe anticoagulants, FXIIa has received a great deal of interest in recent years. In the present study, we employed virtual high-throughput screening of 500,064 compounds within Enamine database to acquire the most potential inhibitors of FXIIa. Subsequently, 18 compounds with significant binding energy (from -65.195 to -15.726 kcal/mol) were selected, and their ADMET properties were predicted to select representative inhibitors. Three compounds (Z1225120358, Z432246974, and Z146790068) exhibited excellent binding affinity and druggability. MD simulation for FXIIa-ligand complexes was carried out to reveal the stability and inhibition mechanism of these three compounds. Through the inhibition of activated factor XIIa assay, we tested the activity of five compounds Z1225120358, Z432246974, Z45287215, Z30974175, and Z146790068, with pIC50 values of 9.3∗10⁻⁷, 3.0∗10⁻⁵, 7.8∗10⁻⁷, 8.7∗10⁻⁷, and 1.3∗10⁻⁶ M, respectively; the AMDET properties of Z45287215 and Z30974175 show not well but have better inhibition activity. We also found that compounds Z1225120358, Z45287215, Z30974175, and Z146790068 could be more inhibition of FXIIa than Z432246974. Collectively, compounds Z1225120358, Z45287215, Z30974175, and Z146790068 were anticipated to be promising drug candidates for inhibition of FXIIa.

Phase Transformation Dynamics in Sulfate-Loaded Lanthanide Triphosphonates. Proton Conductivity and Application as Fillers in PEMFCs

Phase transformation dynamics and proton conduction properties are reported for cationic layer-featured coordination polymers derived from the combination of lanthanide ions (Ln3+) with nitrilo-tris(methylenephosphonic acid) (H6NMP) in the presence of sulfate ions. Two families of materials are isolated and structurally characterized, i.e., [Ln2(H4NMP)2(H2O)4](HSO4)2·nH2O (Ln = Pr, Nd, Sm, Eu, Gd, Tb, Er, Yb; n = 4-5, Series I) and [Ln(H5NMP)]SO4·2H2O (Ln = Pr, Nd, Eu, Gd, Tb; Series II). Eu/Tb bimetallic solid solutions are also prepared for photoluminescence studies. Members of families I and II display high proton conductivity (10-3 and 10-2 S·cm-1 at 80 °C and 95% relative humidity) and are studied as fillers for Nafion-based composite membranes in PEMFCs, under operating conditions. Composite membranes exhibit higher power and current densities than the pristine Nafion membrane working in the range of 70-90 °C and 100% relative humidity and with similar proton conductivity.

The critical role of QM/MM X-ray refinement and accurate tautomer/protomer determination in structure-based drug design

Conventional protein:ligand crystallographic refinement uses stereochemistry restraints coupled with a rudimentary energy functional to ensure the correct geometry of the model of the macromolecule-along with any bound ligand(s)-within the context of the experimental, X-ray density. These methods generally lack explicit terms for electrostatics, polarization, dispersion, hydrogen bonds, and other key interactions, and instead they use pre-determined parameters (e.g. bond lengths, angles, and torsions) to drive structural refinement. In order to address this deficiency and obtain a more complete and ultimately more accurate structure, we have developed an automated approach for macromolecular refinement based on a two layer, QM/MM (ONIOM) scheme as implemented within our DivCon Discovery Suite and "plugged in" to two mainstream crystallographic packages: PHENIX and BUSTER. This implementation is able to use one or more region layer(s), which is(are) characterized using linear-scaling, semi-empirical quantum mechanics, followed by a system layer which includes the balance of the model and which is described using a molecular mechanics functional. In this work, we applied our Phenix/DivCon refinement method-coupled with our XModeScore method for experimental tautomer/protomer state determination-to the characterization of structure sets relevant to structure-based drug design (SBDD). We then use these newly refined structures to show the impact of QM/MM X-ray refined structure on our understanding of function by exploring the influence of these improved structures on protein:ligand binding affinity prediction (and we likewise show how we use post-refinement scoring outliers to inform subsequent X-ray crystallographic efforts). Through this endeavor, we demonstrate a computational chemistry ↔ structural biology (X-ray crystallography) "feedback loop" which has utility in industrial and academic pharmaceutical research as well as other allied fields.

Testing automatic methods to predict free binding energy of host-guest complexes in SAMPL7 challenge

The design of new host–guest complexes represents a fundamental challenge in supramolecular chemistry. At the same time, it opens new opportunities in material sciences or biotechnological applications. A computational tool capable of automatically predicting the binding free energy of any host–guest complex would be a great aid in the design of new host systems, or to identify new guest molecules for a given host. We aim to build such a platform and have used the SAMPL7 challenge to test several methods and design a specific computational pipeline. Predictions will be based on machine learning (when previous knowledge is available) or a physics-based method (otherwise). The formerly delivered predictions with an RMSE of 1.67 kcal/mol but will require further work to identify when a specific system is outside of the scope of the model. The latter is combines the semiempirical GFN2B functional, with docking, molecular mechanics, and molecular dynamics. Correct predictions (RMSE of 1.45 kcal/mol) are contingent on the identification of the correct binding mode, which can be very challenging for host–guest systems with a large number of degrees of freedom. Participation in the blind SAMPL7 challenge provided fundamental direction to the project. More advanced versions of the pipeline will be tested against future SAMPL challenges.

Enhancement of Lipolysis in 3T3-L1 Adipocytes by Nitroarene Capsaicinoid Analogs

Transient receptor potential vanilloid 1 (TRPV1) activation by capsaicin binding increased intracellular calcium influx and stimulated adipocyte-to-adipocyte communication, leading to lipolysis. Generally, enhancement of π-stacking capabilities improves certain binding interactions. Notably, nitroarenes exhibit strong binding interactions with aromatic amino acid side chains in proteins. New capsaicinoid analogs were designed by substitution of the OCH3 group with a nitrogen dioxide (NO2) group on the vanillyl ring to investigate how π-stacking interactions in capsaicinoid analogs contribute to lipolysis. Capsaicinoid analogs, nitro capsaicin (5), and nitro dihydrocapsaicin (6) were prepared in moderate yields via coupling of a nitroaromatic amine salt and fatty acids. Oil Red O staining and triglyceride assays with 10 µM loading of capsaicin (CAP), dihydrocapsaicin (DHC), 5, and 6 were performed to investigate their effect on lipolysis in 3T3-L1 adipocytes. Both assay results indicated that 5 and 6 decreased lipid accumulation by 13.6% and 14.7%, respectively, and significantly reduced triglyceride content by 26.9% and 28.4%, respectively, in comparison with the control experiment. Furthermore, the decrease in triglyceride content observed in response to nitroarene capsaicinoid analogs was approximately 2-folds higher than that of CAP and DHC. These results arose from the NO2 group augmented π-π stacking with Tyr511 and the attractive charge interaction with Glu570 affecting binding interactions with TRPV1 receptors.

Discovery of hCES2A inhibitors from Glycyrrhiza inflata via combination of docking-based virtual screening and fluorescence-based inhibition assays

An integrated strategy via combination of chemical profiling, docking-based virtual screening and fluorescence-based high-throughput inhibitor screening assays was used to efficiently identify natural hCES2A inhibitors from herbal medicines.

Dalbavancin binds ACE2 to block its interaction with SARS-CoV-2 spike protein and is effective in inhibiting SARS-CoV-2 infection in animal models

Infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic worldwide. Currently, however, no effective drug or vaccine is available to treat or prevent the resulting coronavirus disease 2019 (COVID-19). Here, we report our discovery of a promising anti-COVID-19 drug candidate, the lipoglycopeptide antibiotic dalbavancin, based on virtual screening of the FDA-approved peptide drug library combined with in vitro and in vivo functional antiviral assays. Our results showed that dalbavancin directly binds to human angiotensin-converting enzyme 2 (ACE2) with high affinity, thereby blocking its interaction with the SARS-CoV-2 spike protein. Furthermore, dalbavancin effectively prevents SARS-CoV-2 replication in Vero E6 cells with an EC50 of ~12 nM. In both mouse and rhesus macaque models, viral replication and histopathological injuries caused by SARS-CoV-2 infection are significantly inhibited by dalbavancin administration. Given its high safety and long plasma half-life (8-10 days) shown in previous clinical trials, our data indicate that dalbavancin is a promising anti-COVID-19 drug candidate.

Bioactive Conformational Ensemble Server and Database. A Public Framework to Speed Up In Silico Drug Discovery

Modern high-throughput structure-based drug discovery algorithms consider ligand flexibility, but typically with low accuracy, which results in a loss of performance in the derived models. Here we present the bioactive conformational ensemble (BCE) server and its associated database. The server creates conformational ensembles of drug-like ligands and stores them in the BCE database, where a variety of analyses are offered to the user. The workflow implemented in the BCE server combines enhanced sampling molecular dynamics with self-consistent reaction field quantum mechanics (SCRF/QM) calculations. The server automatizes all of the steps to transform one-dimensional (1D) or 2D representation of drugs into 3D molecules, which are then titrated, parametrized, hydrated, and optimized before being subjected to Hamiltonian replica-exchange (HREX) molecular dynamics simulations. Ensembles are collected and subjected to a clustering procedure to derive representative conformers, which are then analyzed at the SCRF/QM level of theory. All structural data are organized in a noSQL database accessible through a graphical interface and in a programmatic manner through a REST API. The server allows the user to define a private workspace and offers a deposition protocol as well as input files for "in house" calculations in those cases where confidentiality is a must. The database and the associated server are available at https://mmb.irbbarcelona.org/BCE.

MovableType Software for Fast Free Energy-Based Virtual Screening: Protocol Development, Deployment, Validation, and Assessment

For decades, the complicated energy surfaces found in macromolecular protein:ligand structures, which require large amounts of computational time and resources for energy state sampling, have been an inherent obstacle to fast, routine free energy estimation in industrial drug discovery efforts. Beginning in 2013, the Merz research group addressed this cost with the introduction of a novel sampling methodology termed “Movable Type” (MT). Using numerical integration methods, the MT method reduces the computational expense for energy state sampling by independently calculating each atomic partition function from an initial molecular conformation in order to estimate the molecular free energy using ensembles of the atomic partition functions. In this work, we report a software package, the DivCon Discovery Suite with the MovableType module from QuantumBio Inc., that performs this MT free energy estimation protocol in a fast, fully encapsulated manner. We discuss the computational procedures and improvements to the original work, and we detail the corresponding settings for this software package. Finally, we introduce two validation benchmarks to evaluate the overall robustness of the method against a broad range of protein:ligand structural cases. With these publicly available benchmarks, we show that the method can use a variety of input types and parameters and exhibits comparable predictability whether the method is presented with “expensive” X-ray structures or “inexpensively docked” theoretical models. We also explore some next steps for the method. The MovableType software is available at http://www.quantumbioinc.com/

Assessing the Performance of Mixed Strategies To Combine Lipophilic Molecular Similarity and Docking in Virtual Screening

The accuracy of structure-based (SB) virtual screening (VS) is heavily affected by the scoring function used to rank a library of screened compounds. Even in cases where the docked pose agrees with the experimental binding mode of the ligand, the limitations of current scoring functions may lead to sensible inaccuracies in the ability to discriminate between actives and inactives. In this context, the combination of SB and ligand-based (LB) molecular similarity may be a promising strategy to increase the hit rates in VS. This study explores different strategies that aim to exploit the synergy between LB and SB methods in order to mitigate the limitations of these techniques, and to enhance the performance of VS studies by means of a balanced combination between docking scores and three-dimensional (3D) similarity. Particularly, attention is focused to the use of measurements of molecular similarity with PharmScreen, which exploits the 3D distribution of atomic lipophilicity determined from quantum mechanical-based continuum solvation calculations performed with the MST model, in conjunction with three docking programs: Glide, rDock, and GOLD. Different strategies have been explored to combine the information provided by docking and similarity measurements for re-ranking the screened ligands. For a benchmarking of 44 datasets, including 41 targets, the hybrid methods increase the identification of active compounds, according to the early (ROCe%) and total (AUC) enrichment metrics of VS, compared to pure LB and SB methods. Finally, the hybrid approaches are also more effective in enhancing the chemical diversity of active compounds. The datasets employed in this work are available in https://github.com/Pharmacelera/Molecular-Similarity-and-Docking.

Discovery of Lonafarnib-Like Compounds: Pharmacophore Modeling and Molecular Dynamics Studies

Progeria is a globally noticed rare genetic disorder manifested by premature aging with no effective treatment. Under these circumstances, farnesyltransferase inhibitors (FTIs) are marked as promising drug candidates. Correspondingly, a pharmacophore model was generated exploiting the features of lonafarnib. The selected pharmacophore model was allowed to screen the InterBioScreen natural compound database to retrieve the potential lead candidates. A series of filtering steps were applied to assess the drug-likeness of the compounds. The obtained compounds were advanced to molecular docking employing the CDOCKER module available with Discovery Studio (DS). Subsequently, three compounds (Hits) have displayed a higher dock score and demonstrated key residue interactions with stable molecular dynamics simulation results compared to the reference compound. Taken together, we therefore put forth three identified Hits as FTIs that may further serve as chemical spaces in designing new compounds.

A model for gain of function in superoxide dismutase

Studies have found that mutant, misfolded superoxide dismutase [Cu–Zn] (SOD1) can convert wild type SOD1 (wtSOD1) in a prion-like fashion, and that misfolded wtSOD1 can be propagated by release and uptake of protein aggregates. In developing a prion-like mechanism for this propagation of SOD1 misfolding we have previously shown how enervation of the SOD1 electrostatic loop (ESL), caused by the formation of transient non-obligate SOD1 oligomers, can lead to an experimentally observed gain of interaction (GOI) that results in the formation of SOD1 amyloid-like filaments. It has also been shown that freedom of ESL motion is essential to catalytic function. This work investigates the possibility that restricting ESL mobility might not only compromise superoxide catalytic activity but also serve to promote the peroxidase activity of SOD1, thus implicating the formation of SOD1 oligomers in both protein misfolding and in protein oxidation.

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A free energy surface for the peroxidase mechanism of superoxide dismutase (SOD1) has been calculated.

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A mechanism that implicates the restriction of mobility in the SOD1 electrostatic loop in protein oxidation is proposed.

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The proxidant nature of bicarbonate, or dissolved carbon dioxide, is investigated.

P, Kumar BRP, Suresh B. Curcumin to inhibit binding of spike glycoprotein to ACE2 receptors: computational modelling, simulations, and ADMET studies to explore curcuminoids against novel SARS-CoV-2 targets

The significant role of curcumin against SARS-CoV-2 drug targets to thwart virus replication and binding into the host system using the computational biology paradigm approach.

Deep Learning-Based Structure-Activity Relationship Modeling for Multi-Category Toxicity Classification: A Case Study of 10K Tox21 Chemicals With High-Throughput Cell-Based Androgen Receptor Bioassay Data

Deep learning (DL) has attracted the attention of computational toxicologists as it offers a potentially greater power for in silico predictive toxicology than existing shallow learning algorithms. However, contradicting reports have been documented. To further explore the advantages of DL over shallow learning, we conducted this case study using two cell-based androgen receptor (AR) activity datasets with 10K chemicals generated from the Tox21 program. A nested double-loop cross-validation approach was adopted along with a stratified sampling strategy for partitioning chemicals of multiple AR activity classes (i.e., agonist, antagonist, inactive, and inconclusive) at the same distribution rates amongst the training, validation and test subsets. Deep neural networks (DNN) and random forest (RF), representing deep and shallow learning algorithms, respectively, were chosen to carry out structure-activity relationship-based chemical toxicity prediction. Results suggest that DNN significantly outperformed RF (p < 0.001, ANOVA) by 22–27% for four metrics (precision, recall, F-measure, and AUPRC) and by 11% for another (AUROC). Further in-depth analyses of chemical scaffolding shed insights on structural alerts for AR agonists/antagonists and inactive/inconclusive compounds, which may aid in future drug discovery and improvement of toxicity prediction modeling.

Virtual screening of National Cancer Institute database for claudin-4 inhibitors: Synthesis, biological evaluation, and molecular dynamics studies

Claudin-4 (CLDN4) is a vital member of tight-junction proteins that is often overexpressed in cancer and other malignancies. The three-dimensional structure of human CLDN4 was constructed based on homology modeling approach. A total of 265 242 molecules from the National Cancer Institute (NCI) database has been utilized as a dataset for this study. In the present work, structure-based virtual screening is performed with the NCI database using Glide. By molecular docking, 10 candidate molecules with high scoring functions, which binds to the active site of CLDN4 were identified. Subsequently, molecular dynamics simulations of membrane protein were used for optimization of the top-three lead compounds (NCI110039, NCI344682, and NCI661251) with CLDN4 in a dynamic system. The lead molecule from NCI database NCI11039 (purpurogallin carboxylic acid) was synthesized and cytotoxic properties were evaluated with A549, MCF7 cell lines. Our docking and dynamics simulations predicted that ARG31, ASN142, ASP146, and ARG158 as critically important residues involved in the CLDN4 activity. Finally, three lead candidates from the NCI database were identified as potent CLDN4 inhibitors. Cytotoxicity assays had proved that purpurogallin carboxylic acid had an inhibitory effect towards breast (MCF7) and lung (A549) cancer cell lines. Computational insights and in vitro (cytotoxicity) studies reported in this study are expected to be helpful for the development of novel anticancer agents.