Exploring the Binding Mechanism of Metabotropic Glutamate Receptor 5 Negative Allosteric Modulators in Clinical Trials by Molecular Dynamics Simulations

Phylogeny and evolution of SARS-CoV-2 during Delta and Omicron variant waves in India

SARS-CoV-2 evolution has continued to generate variants, responsible for new pandemic waves locally and globally. Varying disease presentation and severity has been ascribed to inherent variant characteristics and vaccine immunity. This study analyzed genomic data from 305 whole genome sequences from SARS-CoV-2 patients before and through the third wave in India. Delta variant was reported in patients without comorbidity (97%), while Omicron BA.2 was reported in patients with comorbidity (77%). Tissue adaptation studies brought forth higher propensity of Omicron variants to bronchial tissue than lung, contrary to observation in Delta variants from Delhi. Study of codon usage pattern distinguished the prevalent variants, clustering them separately, Omicron BA.2 isolated in February grouped away from December strains, and all BA.2 after December acquired a new mutation S959P in ORF1b (44.3% of BA.2 in the study) indicating ongoing evolution. Loss of critical spike mutations in Omicron BA.2 and gain of immune evasion mutations including G142D, reported in Delta but absent in BA.1, and S371F instead of S371L in BA.1 could explain very brief period of BA.1 in December 2021, followed by complete replacement by BA.2. Higher propensity of Omicron variants to bronchial tissue, probably ensured increased transmission while Omicron BA.2 became the prevalent variant possibly due to evolutionary trade-off. Virus evolution continues to shape the epidemic and its culmination.Communicated by Ramaswamy H. Sarma.

Exploring biased activation characteristics by molecular dynamics simulation and machine learning for the μ-opioid receptor

Biased ligands selectively activating specific downstream signaling pathways (termed as biased activation) exhibit significant therapeutic potential. However, the conformational characteristics revealed are very limited for the biased activation, which is not conducive to biased drug development. Motivated by the issue, we combine extensive accelerated molecular dynamics simulations and an interpretable deep learning model to probe the biased activation features for two complex systems constructed by the inactive μOR and two different biased agonists (G-protein-biased agonist TRV130 and β-arrestin-biased agonist endomorphin2). The results indicate that TRV130 binds deeper into the receptor core compared to endomorphin2, located between W2936.48 and D1142.50, and forms hydrogen bonding with D1142.50, while endomorphin2 binds above W2936.48. The G protein-biased agonist induces greater outward movements of the TM6 intracellular end, forming a typical active conformation, while the β-arrestin-biased agonist leads to a smaller extent of outward movements of TM6. Compared with TRV130, endomorphin2 causes more pronounced inward movements of the TM7 intracellular end and more complex conformational changes of H8 and ICL1. In addition, important residues determining the two different biased activation states were further identified by using an interpretable deep learning classification model, including some common biased activation residues across Class A GPCRs like some key residues on the TM2 extracellular end, ECL2, TM5 intracellular end, TM6 intracellular end, and TM7 intracellular end, and some specific important residues of ICL3 for μOR. The observations will provide valuable information for understanding the biased activation mechanism for GPCRs.

Exploring novel lead scaffolds for SGLT2 inhibitors: Insights from machine learning and molecular dynamics simulations

Sodium-glucose cotransporter 2 (SGLT2) plays a pivotal role in mediating glucose reabsorption within the renal filtrate, representing a well-known target in type 2 diabetes and heart failure. Recent emphasis has been directed toward designing SGLT2 inhibitors, with C-glycoside inhibitors emerging as front-runners. The architecture of SGLT2 has been successfully resolved using cryo-electron microscopy. However, comprehension of the pharmacophores within the binding site of SGLT2 remains unclear. Here, we use machine learning and molecular dynamics simulations on SGLT2 bound with its inhibitors in preclinical or clinical development to shed light on this issue. Our dataset comprises 1240 SGLT2 inhibitors amalgamated from diverse sources, forming the basis for constructing machine learning models. SHapley Additive exPlanation (SHAP) elucidates the crucial fragments that contribute to inhibitor activity, specifically Morgan_3, 162, 310, 325, 366, 470, 597, 714, 926, and 975. Furthermore, the computed binding free energies and per-residue contributions for SGLT2-inhibitor complexes unveil crucial fragments of inhibitors that interact with residues Asn-75, His-80, Val-95, Phe-98, Val-157, Leu-274, and Phe-453 in the binding site of SGLT2. This comprehensive investigation enhances understanding of the binding mechanism for SGLT2 inhibitors, providing a robust framework for evaluating and discovering novel lead scaffolds within this domain.

Identification of natural antimicrobial peptides mimetic to inhibit Ca2+ influx DDX3X activity for blocking dengue viral infectivity

Viruses are microscopic biological entities that can quickly invade and multiply in a living organism. Each year, over 36,000 people die and nearly 400 million are infected with the dengue virus (DENV). Despite dengue being an endemic disease, no targeted and effective antiviral peptide resource is available against the dengue species. Antiviral peptides (AVPs) have shown tremendous ability to fight against different viruses. Accelerating antiviral drug discovery is crucial, particularly for RNA viruses. DDX3X, a vital cell component, supports viral translation and interacts with TRPV4, regulating viral RNA metabolism and infectivity. Its diverse signaling pathway makes it a potential therapeutic target. Our study focuses on inhibiting viral RNA translation by blocking the activity of the target gene and the TRPV4-mediated Ca2+ cation channel. Six major proteins from camel milk were first extracted and split with the enzyme pepsin. The antiviral properties were then analyzed using online bioinformatics programs, including AVPpred, Meta-iAVP, AMPfun, and ENNAVIA. The stability of the complex was assessed using MD simulation, MM/GBSA, and principal component analysis. Cytotoxicity evaluations were conducted using COPid and ToxinPred. The top ten AVPs, determined by optimal scores, were selected and saved for docking studies with the GalaxyPepDock tools. Bioinformatics analyses revealed that the peptides had very short hydrogen bond distances (1.8 to 3.6 Å) near the active site of the target protein. Approximately 76% of the peptide residues were 5-11 amino acids long. Additionally, the identified peptide candidates exhibited desirable properties for potential therapeutic agents, including a net positive charge, moderate toxicity, hydrophilicity, and selectivity. In conclusion, this computational study provides promising insights for discovering peptide-based therapeutic agents against DENV.

Assessing the role of residue Phe108 of cytochrome P450 3A4 in allosteric effects of midazolam metabolism

Cytochrome P450 3A4 (CYP3A4) is involved in the metabolism of more drugs in clinical use than any other xenobiotic-metabolizing enzyme. CYP3A4-mediated drug metabolism is usually allosterically modulated by substrate concentration (homotropic allostery) and other drugs (heterotropic allostery), exhibiting unusual kinetic profiles and regiospecific metabolism. Recent studies suggest that residue Phe108 (F108) of CYP3A4 may have an important role in drug metabolism. In this work, residue mutations were coupled with well-tempered metadynamics simulations to assess the importance of F108 in the allosteric effects of midazolam metabolism. Comparing the simulation results of the wild-type and mutation systems, we identify that the π-π interaction and steric effect between the F108 side chain and midazolam is favorable for the stable binding of substrate in the active site. F108 also plays an important role in the transition of substrate binding mode, which mainly induces the transition of substrate binding mode by forming π-π interactions with multiple aromatic rings of the substrate. Moreover, the side chain of F108 is closely related to the radius and depth of the 2a and 2f channels, and F108 may further regulate drug metabolism by affecting the pathway, orientation, or time of substrate entry into the CYP3A4 active site or product egress from the active site. Altogether, we suggest that F108 affects drug metabolism and regulatory mechanisms by affecting substrate binding stability, binding mode transition, and channel characteristics of CYP3A4. Our findings could promote the understanding of complicated allosteric mechanisms in CYP3A4-mediated drug metabolism, and the knowledge could be used for drug development and disease treatment.

Identification of thrombin inhibiting antithrombin-III like protein from Punica granatum using in silico approach and in vitro validation of thrombin inhibition activity in crude protein

Thrombosis is characterized by the formation of clots in the blood vessels. Antithrombin-III deficiency in the blood causes thrombus formation. Supplementing antithrombin-III may serve as anticoagulant therapy. In the present studies, an antithrombin like Protein from Punica granatum has been identified and characterized using in silico approach. Based on sequence homology, an ALPP was selected depending upon its highest binding affinity of -41.28 kcal/mol with thrombin. Thrombin structure complexed with ALPP was docked with TAME using AutoDock Vina. No binding was observed for TAME at Ser195 of thrombin. MD simulation (50 ns) was performed to evaluate the flexibility and stability of docked complexes. In vitro assays with crude protein showed 78% thrombin inhibition at 5 µg and calculated IC50 value was 0.188 µg. The presence of thrombin inhibitors in crude protein was also confirmed by reverse zymography. Thus, it is very likely that the protein identified from P. granatum may act as thrombin inhibitor.

Mechanism of XiJiaQi in the treatment of chronic heart failure: Integrated analysis by pharmacoinformatics, molecular dynamics simulation, and SPR validation

OBJECTIVE

Chronic heart failure (CHF) is a complicated clinical syndrome with a high mortality rate. XiJiaQi (XJQ) is a traditional Chinese medicine used in the clinical treatment of CHF, but its bioactive components and their modes of action remain unknown. This study was designed to unravel the molecular mechanism of XJQ in the treatment of CHF using multiple computer-assisted and experimental methods.

METHODS

Pharmacoinformatics-based methods were used to explore the active components and targets of XJQ in the treatment of CHF. ADMETlab was then utilized to evaluate the pharmacokinetic and toxicological properties of core components. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were to explore the underlying mechanism of XJQ treatment. Molecular docking, surface plasmon resonance (SPR), and molecular dynamics (MD) were employed to evaluate the binding of active components to putative targets.

RESULTS

Astragaloside IV, formononetin, kirenol, darutoside, periplocin and periplocymarin were identified as core XJQ-related components, and IL6 and STAT3 were identified as core XJQ targets. ADME/T results indicated that periplocin and periplocymarin may have potential toxicity. GO and KEGG pathway analyses revealed that XJQ mainly intervenes in inflammation, apoptosis, diabetes, and atherosclerosis-related biological pathways. Molecular docking and SPR revealed that formononetin had a high affinity with IL6 and STAT3. Furthermore, MD simulation confirmed that formononetin could firmly bind to the site 2 region of IL6 and the DNA binding domain of STAT3.

CONCLUSION

This study provides a mechanistic rationale for the clinical application of XJQ. Modulation of STAT3 and IL-6 by XJQ can impact CHF, further guiding research efforts into the molecular underpinnings of CHF.

Identification and molecular docking of novel chikungunya virus NSP4 inhibitory peptides from camel milk proteins

The chikungunya (CHIK) virus is an arbovirus belonging to the alphavirus (Togaviridae family). Around 85% of infected individuals suffer from symptoms such as high fever and severe joint pain; about 30 to 40% will develop a chronic joint illness. The Nsp4 protease is the most conserved protein in the alphavirus family and serves as an RNA-dependent RNA polymerase (RdRp). Targeting this enzyme might inhibit the CHIKV replication cycle. This work aims to in silico study the CHIKV RdRp inhibitory effect of peptides derived from camel milk protein as antiviral peptides. Various bioinformatics tools were recruited to identify, screen, predict and assess peptides obtained from camel milk as antiviral peptides (AVPs). During this study, CHIKV Nsp4 (polymerase) was used as a target to be inhibited by interaction with peptides derived from camel milk protein. Among 91 putative bioactive peptides, the best predicted 5 were further evaluated. Molecular docking showed that the top 5 AVPs generated better docking scores and interacted well with active sites of Nsp4 by the formation of different hydrogen bonds as well as other bonds. AVP63 and AVP20 showed the best Molecular docking and MD simulation results. The residue 315ASP of the GDD motif (catalytic core) exhibited a favorable interaction with the AVPs. The findings of this study suggest that the AVP20 derived from camel milk protein can be a potential novel CHIKV polymerase inhibitor.Communicated by Ramaswamy H. Sarma.

ALPACA: A machine Learning Platform for Affinity and selectivity profiling of CAnnabinoids receptors modulators

The development of small molecules that selectively target the cannabinoid receptor subtype 2 (CB2R) is emerging as an intriguing therapeutic strategy to treat neurodegeneration, as well as to contrast the onset and progression of cancer. In this context, in-silico tools able to predict CB2R affinity and selectivity with respect to the subtype 1 (CB1R), whose modulation is responsible for undesired psychotropic effects, are highly desirable. In this work, we developed a series of machine learning classifiers trained on high-quality bioactivity data of small molecules acting on CB2R and/or CB1R extracted from ChEMBL v30. Our classifiers showed strong predictive power in accurately determining CB2R affinity, CB1R affinity, and CB2R/CB1R selectivity. Among the built models, those obtained using random forest as algorithm proved to be the top-performing ones (AUC in validation ≥0.96) and were made freely accessible through a user-friendly web platform developed ad hoc and called ALPACA (https://www.ba.ic.cnr.it/softwareic/alpaca/). Due to its user-friendly interface and robust predictive power, ALPACA can be a valuable tool in saving both time and resources involved in the design of selective CB2R modulators.

Exploring the natural products chemical space through a molecular search to discover potential inhibitors that target the hypoxia-inducible factor (HIF) prolyl hydroxylase domain (PHD)

Introduction: Hypoxia-inducible factor (HIF) prolyl hydroxylase domain (PHD) enzymes are major therapeutic targets of anemia and ischemic/hypoxia diseases. To overcome safety issues, liver failure, and problems associated with on-/off-targets, natural products due to their novel and unique structures offer promising alternatives as drug targets. Methods: In the current study, the Marine Natural Products, North African, South African, East African, and North-East African chemical space was explored for HIF-PHD inhibitors discovery through molecular search, and the final hits were validated using molecular simulation and free energy calculation approaches. Results: Our results revealed that CMNPD13808 with a docking score of -8.690 kcal/mol, CID15081178 with a docking score of -8.027 kcal/mol, CID71496944 with a docking score of -8.48 kcal/mol and CID11821407 with a docking score of -7.78 kcal/mol possess stronger activity than the control N-[(4-hydroxy-8-iodoisoquinolin-3-yl)carbonyl]glycine, 4HG (-6.87 kcal/mol). Interaction analysis revealed that the target compounds interact with Gln239, Tyr310, Tyr329, Arg383 and Trp389 residues, and chelate the active site iron in a bidentate manner in PHD2. Molecular simulation revealed that these target hits robustly block the PHD2 active site by demonstrating stable dynamics. Furthermore, the half-life of the Arg383 hydrogen bond with the target ligands, which is an important factor for PHD2 inhibition, remained almost constant in all the complexes during the simulation. Finally, the total binding free energy of each complex was calculated as CMNPD13808-PHD2 -72.91 kcal/mol, CID15081178-PHD2 -65.55 kcal/mol, CID71496944-PHD2 -68.47 kcal/mol, and CID11821407-PHD2 -62.06 kcal/mol, respectively. Conclusion: The results show the compounds possess good activity in contrast to the control drug (4HG) and need further in vitro and in vivo validation for possible usage as potential drugs against HIF-PHD2-associated diseases.

Deciphering the intrinsic dynamics of unphosphorylated IRAK4 kinase bound to type I and type II inhibitors

Interleukin-1 receptor-associated kinase 4 (IRAK4) is a vital protein involved in Toll-like and interleukin-1 receptor signal transduction. Several studies have reported regarding the crystal structure, dynamic properties, and interactions with inhibitors of the phosphorylated form of IRAK4. However, no dynamic properties of inhibitor-bound unphosphorylated IRAK4 have been previously studied. Herein, we report the intrinsic dynamics of unphosphorylated IRAK4 (uIRAK4) bound to type I and type II inhibitors. The corresponding apo and inhibitor-bound forms of uIRAK4 were subjected to three independent simulations of 500 ns (total 1.5 μs) each, and their trajectories were analyzed. The results indicated that all three systems were relatively stable, except for the type II inhibitor-bound form of uIRAK4, which exhibited less compact folding and higher solvent surface area. The intra-hydrogen bonds corroborated the structural deformation of the type-II inhibitor-bound complex, which could be attributed to the long molecular structure of the type-II inhibitor. Moreover, the type II inhibitor bound to uIRAK4 showed higher binding free energy with uIRAK4 than the type I inhibitor. The free energy landscape analysis showed a reorientation of Phe330 side chain from the DFG motif at different metastable states for all the systems. The intra-residual distance between residues Lys213, Glu233, Tyr262, and Phe330 suggests a functional interplay when the inhibitors are bound to uIRAK4, thereby hinting at their crucial role in the inhibition mechanism. Ultimately, the intrinsic dynamics study observed between type I/II inhibitor-bound forms of uIRAK4 may assist in better understanding the enzyme and designing therapeutic compounds.

Multi-state Model-Based Identification of Cryptic Allosteric Sites on Human Serotonin Transporter

Serotonin transporter (SERT) plays a fundamental role in taking the synaptic cleft serotonin back to the presynaptic neuron. The discovery of allosteric SERT modulators represents the next-generation medication for psychiatric disorders such as depression. Here, based on the cryo-EM structures of ibogaine in complex with SERT in distinct conformations, the multiple functional structures of the transporter bound to serotonin, including outward-open (OO_holo), outward-occluded (OC_holo), and inward-open (IO_holo and IO_holo'), were carefully characterized by induced-fit docking Gaussian-accelerated molecular dynamics (IFD-GaMD) simulation and the free-energy landscape analysis. Further MM/GBSA binding free energy, per-residue contribution, and molecular interaction fingerprint calculations revealed the interaction variations of serotonin with SERT in functional structures, which confirmed the allostery of SERT during serotonin reuptake. Moreover, five unique cryptic allosteric sites, which are druggable and capable of targeting by small molecules, were identified on the characterized multistate structures. These results provide structural and energetic information for the molecular mechanism of serotonin reuptake and will provide opportunities for the development of novel therapeutics based on the identified new allosteric sites on SERT.

A multiple-step screening protocol to identify norepinephrine and dopamine reuptake inhibitors for depression

Depression severely impairs the health of people all over the world. Cognitive dysfunction due to depression has resulted in a severe economic burden to family and society induced by the reduction of social functioning of patients. Norepinephrine-dopamine reuptake inhibitors (NDRIs) targeted with the human norepinephrine transporter (hNET) and distributed with the human dopamine transporter (hDAT) simultaneously treat depression and improve cognitive function, and they effectively prevent sexual dysfunction and other side effects. Because many patients continue to poorly respond to NDRIs, it is urgent to discover novel NDRI antidepressants that do not interfere with cognitive function. The aim of this work was to selectively identify novel NDRI candidates acting against hNET and hDAT from large compound libraries by a comprehensive strategy integrating support vector machine (SVM) models, ADMET, molecular docking, in vitro binding assays, molecular dynamics simulation, and binding energy calculation. First, 6522 compounds that do not inhibit the human serotonin transporter (hSERT) were obtained by SVM models of hNET, hDAT, and non-target hSERT with similarity analyses from compound libraries. ADMET and molecular docking were then used to identify compounds that could potently bind to the hNET and hDAT with satisfactory ADMET, and 4 compounds were successfully identified. According to their docking scores and ADMET information, 3719810 was advanced for profiling by in vitro assays as a novel NDRI lead compound due to its strongest druggability and balancing activities. Encouragingly, 3719810 performed comparative activities on two targets, with Ki values of 7.32 μM for hNET and 5.23 μM for hDAT. To obtain candidates with additional activities and balance the activities of 2 targets, 5 analogs were optimized, and 2 novel scaffold compounds were successively designed. By assessment of molecular docking, molecular dynamics simulations, and binding energy calculations, 5 compounds were validated as NDRI candidates with high activities, and 4 of them performed acceptable balancing activities acting on hNET and hDAT. This work supplied promising novel NDRIs for treatment of depression with cognitive dysfunction or other related neurodegenerative disorders, and also provided a strategy for highly efficient and cost-effective identification of inhibitors for dual targets with homologous non-targets.

Deciphering the mechanism of resistance by novel double mutations in pncA in Mycobacterium tuberculosis using protein structural graphs (PSG) and structural bioinformatic approaches

The evolution of MDR and XDR-TB is a growing concern and public health safety threat around the world. Gene mutations are the prime cause of drug resistance in tuberculosis, however the reports of double mutations further aggravated the situation. Despite the large-scale genomic sequencing and identification of novel mutations, structure investigation of the protein is still required to structurally and functionally characterize these novel mutations to design novel drugs for improved clinical outcome. Hence, we used structural bioinformatics approaches i.e. molecular modeling, residues communication and molecular simulation to understand the impact of novel double S59Y-L85P, D86G-V180F and S104G-V130 M mutation on the structure, function of pncA encoded Pyrazinamidase (PZase) and resistance of Pyrazinamide (PZA). Our results revealed that these mutations alter the binding paradigm and destabilize the protein to release the drug. Protein commination network (PCN) revealed variations in the hub residues and sub-networks which consequently alter the internal communication and signaling. The region 1-75 demonstrated higher flexibility in the mutant structures and minimal by the wild type which destabilize of the internally arranged beta-sheets which consequently reduce the binding of PZA and potentially Fe ion in the mutants. Hydrogen bonding analysis further validated the findings. The total binding free energy (ΔG) for each complex i.e. wild type -7.46 kcal/mol, S59Y-L85P -5.21 kcal/mol, S104G-V130 M -5.33 kcal/mol while for the D86G-V180F mutant the TBE was calculated to be -6.26 kcal/mol. This further confirms that these mutations reduce the binding energy of PZA for PZase and causes resistance in the effective therapy for TB. The trajectories motion was also observed to be affected by these mutations. In conclusion, these mutations use destabilizing approach to reduce the binding of PZA and causes resistance. These features can be used to design novel structure-based drugs against Tuberculosis.

Unbiased molecular dynamics simulation of a first-in-class small molecule inhibitor binds to oncostatin M

Small molecule inhibitors (SMIs) targeting oncostatin M (OSM) signaling pathway represent new therapeutics to combat cancer, inflammatory bowel disease (IBD) and CNS disease. Recently, the first-in-class SMI named SMI-10B that target OSM and block its interaction with receptor (OSMR) were reported. However, the binding pocket and interaction mode of the compound on OSM remain poorly understood, which hampering the rational design of SMIs that target OSM. Here, using SMI-10B as a probe, the multiple pockets on OSM for small molecules binding were extensively explored by unbiased molecular dynamics (MD) simulations. Then, the near-native structure of the complex was identified by molecular mechanics generalized Born surface area (MM/GBSA) binding energy funnel. Moreover, the binding stabilities of the protein-ligand complexes in near- and non-native conformations were verified by additional independent MD runs and absolute free energy perturbation (FEP) calculation. In summary, the unique feature of SMI-10B spontaneously binds to OSM characterized here not only provide detailed information for understanding the molecular mechanism of SMI-10B binding to OSM, but also will facilitate the rational design of novel and more potent SMIs to block OSM signaling.

A systematic review of state-of-the-art strategies for machine learning-based protein function prediction

New drug discovery is inseparable from the discovery of drug targets, and the vast majority of the known targets are proteins. At the same time, proteins are essential structural and functional elements of living cells necessary for the maintenance of all forms of life. Therefore, protein functions have become the focus of many pharmacological and biological studies. Traditional experimental techniques are no longer adequate for rapidly growing annotation of protein sequences, and approaches to protein function prediction using computational methods have emerged and flourished. A significant trend has been to use machine learning to achieve this goal. In this review, approaches to protein function prediction based on the sequence, structure, protein-protein interaction (PPI) networks, and fusion of multi-information sources are discussed. The current status of research on protein function prediction using machine learning is considered, and existing challenges and prominent breakthroughs are discussed to provide ideas and methods for future studies.

Molecular dynamics study on the behavior and binding mechanism of target protein Transgelin-2 with its agonist TSG12 for anti-asthma drug discovery

Transgelin-2 (TG2) is a novel promising therapeutic target for the treatment of asthma as it plays an important role in relaxing airway smooth muscles and reducing pulmonary resistance in asthma. The compound TSG12 is the only reported TG2 agonist with in vivo anti-asthma activity. However, the dynamic behavior and ligand binding sites of TG2 and its binding mechanism with TSG12 remain unclear. In this study, we performed 12.6 μs molecular dynamics (MD) simulations for apo-TG2 and TG2-TSG12 complex, respectively. The results suggested that the apo-TG2 has 4 most populated conformations, and that its binding of the agonist could expand the conformation distribution space of the protein. The simulations revealed 3 potential binding sites in 3 most populated conformations, one of which is induced by the agonist binding. Free energy decomposition uncovered 8 important residues with contributions stronger than -1 kcal/mol. Computational alanine scanning for the important residues by 100 ns conventional MD simulation for each mutated TG2-TSG12 complexes demonstrated that E27, R49 and F52 are essential residues for the agonist binding. These results should be helpful to understand the dynamic behavior of TG2 and its binding mechanism with the agonist TSG12, which could provide some structural insights into the novel mechanism for anti-asthma drug development.

Discovery of Mcl-1 inhibitors through virtual screening, molecular dynamics simulations and in vitro experiments

As a member of the B-cell lymphoma 2 (Bcl-2) protein family, the myeloid leukemia cell differentiation protein (Mcl-1) can inhibit apoptosis and plays an active role in the process of tumor escape from apoptosis. Therefore, inhibition of Mcl-1 protein can effectively promote the apoptosis of tumor cells and may also reduce tumor cell resistance to drugs targeting other anti-apoptotic proteins. This research is dedicated to the development of Mcl-1 inhibitors, aiming to provide more references for lead compounds with different scaffolds for the development of targeted anticancer drugs. We obtained a series of small molecules with a common core skeleton through molecular docking from Specs database and searched the core structure in ZINC database for more similar small molecules. Collecting these small molecules for preliminary experimental screening, we found a batch of active compounds, and selected two small molecules with the strongest inhibitory activity on B16F10 cells: compound 7 and compound 1. Their IC₅₀s are 7.86 ± 1.25 and 24.72 ± 1.94 μM, respectively. These two compounds were also put into cell scratch test for B16F10 cells and cell viability assay of other cell lines. Furthermore, through molecular dynamics (MD) simulation analysis, we found that compound 7 formed strong binding with the key P2, P3 pocket and ARG 263 of Mcl-1. Finally, ADME results showed that compound 7 performs well in terms of drug similarity. In conclusion, this study provides hits with co-scaffolds that may aid in the design of effective clinical drugs targeting Mcl-1 and the future drug development.

Neohesperidin and spike RBD interaction in omicron and its sub-variants: In silico, structural and simulation studies

COVID-19 caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged first around December 2019 in the city of Wuhan, China. Since then, several variants of the virus have emerged with different biological properties. This pandemic has so far led to widespread infection cycles with millions of fatalities and infections globally. In the recent cycle, a new variant omicron and its three sub-variants BA.1, BA.2 and BA.3 have emerged which seems to evade host immune defences and have brisk infection rate. Particularly, BA.2 variant has shown high transmission rate over BA.1 strain in different countries including India. In the present study, we have evaluated a set of eighty drugs/compounds using in silico docking calculations in omicron and its variants. These molecules were reported previously against SARS-CoV-2. Our docking and simulation analyses suggest differences in affinity of these compounds in omicron and BA.2 compared to SARS-CoV-2. These studies show that neohesperidin, a natural flavonoid found in Citrus aurantium makes a stable interaction with spike receptor domain of omicron and BA.2 compared to other variants. Free energy binding analyses further validates that neohesperidin forms a stable complex with spike RBD in omicron and BA.2 with a binding energy of -237.9 ± 18.7 kJ/mol and -164.1 ± 17.5 kJ/mol respectively. Key residual differences in the RBD interface of these variants form the basis for differential interaction affinities with neohesperidin as drug binding site overlaps with RBD-human ACE2 interface. These data might be useful for the design and development of novel scaffolds and pharmacophores to develop specific therapeutic strategies against these novel variants.

Evaluation of decursin and its isomer decursinol angelate as potential inhibitors of human glutamate dehydrogenase activity through in silico and enzymatic assay screening

Glutaminolysis is a typical hallmark of malignant tumors across different cancers. Glutamate dehydrogenase (GDH, GLUD1) is one such enzyme involved in the conversion of glutamate to α-ketoglutarate. High levels of GDH are associated with numerous diseases and is also a prognostic marker for predicting metastasis in colorectal cancer. Therefore, inhibiting GDH can be a crucial therapeutic target. Here in this study, we performed molecular docking analysis of 8 different plants derived single compounds collected from pubChem database for screening and selected decursin (DN) and decursinol angelate (DA). We performed molecular dynamics simulation (MD), monitored the stability, interaction for protein and docked ligand at 50 ns, and evaluated the molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) free energy calculation on the twoselected compounds along with a standard inhibitor epigallocatechin gallate (EGCG) as reference. The final results showed the formation of stable hydrogen bond interactions by DN and DA in the residues of R400 and Y386 at the ADP activation site of GDH, which was important for the selective inhibition of GDH activity. Additionally, the total binding energy of DN and DA were -115.5 kJ/mol and -106.2 kJ/mol, which was higher than the standard reference GDH inhibitor EGCG (-92.8 kJ/mol). Furthermore, biochemical analysis for GDH inhibition substantiated our computational results and established DN and DA as novel GDH inhibitor. The percentage of IC₅₀ inhibition for DN and DA were 1.035 μM and 1.432 μM. Conclusively, DN and DA can be a novel therapeutic drug for inhibition of glutamate dehydrogenase.

LargeMetabo: an out-of-the-box tool for processing and analyzing large-scale metabolomic data

Large-scale metabolomics is a powerful technique that has attracted widespread attention in biomedical studies focused on identifying biomarkers and interpreting the mechanisms of complex diseases. Despite a rapid increase in the number of large-scale metabolomic studies, the analysis of metabolomic data remains a key challenge. Specifically, diverse unwanted variations and batch effects in processing many samples have a substantial impact on identifying true biological markers, and it is a daunting challenge to annotate a plethora of peaks as metabolites in untargeted mass spectrometry-based metabolomics. Therefore, the development of an out-of-the-box tool is urgently needed to realize data integration and to accurately annotate metabolites with enhanced functions. In this study, the LargeMetabo package based on R code was developed for processing and analyzing large-scale metabolomic data. This package is unique because it is capable of (1) integrating multiple analytical experiments to effectively boost the power of statistical analysis; (2) selecting the appropriate biomarker identification method by intelligent assessment for large-scale metabolic data and (3) providing metabolite annotation and enrichment analysis based on an enhanced metabolite database. The LargeMetabo package can facilitate flexibility and reproducibility in large-scale metabolomics. The package is freely available from https://github.com/LargeMetabo/LargeMetabo.

Transcriptomic discovery of a theranostic signature (SERPINE1/MMP3/COL1A1/SPP1) for head and neck squamous cell carcinomas and identification of antrocinol as a candidate drug

Head and neck squamous cell carcinomas (HNSCC) are prevalent malignancies with a disappointing prognosis, necessitating the search for theranostic biomarkers for better management. Based on a meta-analysis of transcriptomic data containing ten clinical datasets of HNSCC and matched nonmalignant samples, we identified SERPINE1/MMP3/COL1A1/SPP1 as essential hub genes as the potential theranostic biomarkers. Our analysis suggests these hub genes are associated with the extracellular matrix, peptidoglycans, cell migration, wound-healing processes, complement and coagulation cascades, and the AGE-RAGE signaling pathway within the tumor microenvironment. Also, these hub genes were associated with tumor-immune infiltrating cells and immunosuppressive phenotypes of HNSCC. Further investigation of The Cancer Genome Atlas (TCGA) cohorts revealed that these hub genes were associated with staging, metastasis, and poor survival in HNSCC patients. Molecular docking simulations were performed to evaluate binding activities between the hub genes and antrocinol, a novel small-molecule derivative of an anticancer phytochemical antrocin previously discovered by our group. Antrocinol showed high affinities to MMP3 and COL1A1. Notably, antrocinol presented satisfactory drug-like and ADMET properties for therapeutic applications. These results hinted at the potential of antrocinol as an anti-HNSCC candidate via targeting MMP3 and COL1A1. In conclusion, we identified hub genes: SERPINE1/MMP3/COL1A1/SPP1 as potential diagnostic biomarkers and antrocinol as a potential new drug for HNSCC.

DRESIS: the first comprehensive landscape of drug resistance information

Widespread drug resistance has become the key issue in global healthcare. Extensive efforts have been made to reveal not only diverse diseases experiencing drug resistance, but also the six distinct types of molecular mechanisms underlying this resistance. A database that describes a comprehensive list of diseases with drug resistance (not just cancers/infections) and all types of resistance mechanisms is now urgently needed. However, no such database has been available to date. In this study, a comprehensive database describing drug resistance information named 'DRESIS' was therefore developed. It was introduced to (i) systematically provide, for the first time, all existing types of molecular mechanisms underlying drug resistance, (ii) extensively cover the widest range of diseases among all existing databases and (iii) explicitly describe the clinically/experimentally verified resistance data for the largest number of drugs. Since drug resistance has become an ever-increasing clinical issue, DRESIS is expected to have great implications for future new drug discovery and clinical treatment optimization. It is now publicly accessible without any login requirement at: https://idrblab.org/dresis/.

DrugMAP: molecular atlas and pharma-information of all drugs

The efficacy and safety of drugs are widely known to be determined by their interactions with multiple molecules of pharmacological importance, and it is therefore essential to systematically depict the molecular atlas and pharma-information of studied drugs. However, our understanding of such information is neither comprehensive nor precise, which necessitates the construction of a new database providing a network containing a large number of drugs and their interacting molecules. Here, a new database describing the molecular atlas and pharma-information of drugs (DrugMAP) was therefore constructed. It provides a comprehensive list of interacting molecules for >30 000 drugs/drug candidates, gives the differential expression patterns for >5000 interacting molecules among different disease sites, ADME (absorption, distribution, metabolism and excretion)-relevant organs and physiological tissues, and weaves a comprehensive and precise network containing >200 000 interactions among drugs and molecules. With the great efforts made to clarify the complex mechanism underlying drug pharmacokinetics and pharmacodynamics and rapidly emerging interests in artificial intelligence (AI)-based network analyses, DrugMAP is expected to become an indispensable supplement to existing databases to facilitate drug discovery. It is now fully and freely accessible at: https://idrblab.org/drugmap/.

Computational exploration of the dual role of the phytochemical fortunellin: Antiviral activities against SARS-CoV-2 and immunomodulatory abilities against the host

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infections generate approximately one million virions per day, and the majority of available antivirals are ineffective against it due to the virus's inherent genetic mutability. This necessitates the investigation of concurrent inhibition of multiple SARS-CoV-2 targets. We show that fortunellin (acacetin 7-O-neohesperidoside), a phytochemical, is a promising candidate for preventing and treating coronavirus disease (COVID-19) by targeting multiple key viral target proteins. Fortunellin supports protective immunity while inhibiting pro-inflammatory cytokines and apoptosis pathways and protecting against tissue damage. Fortunellin is a phytochemical found in Gojihwadi kwath, an Indian traditional Ayurvedic formulation with an antiviral activity that is effective in COVID-19 patients. The mechanistic action of its antiviral activity, however, is unknown. The current study comprehensively evaluates the potential therapeutic mechanisms of fortunellin in preventing and treating COVID-19. We have used molecular docking, molecular dynamics simulations, free-energy calculations, host target mining of fortunellin, gene ontology enrichment, pathway analyses, and protein-protein interaction analysis. We discovered that fortunellin reliably binds to key targets that are necessary for viral replication, growth, invasion, and infectivity including Nucleocapsid (N-CTD) (-54.62 kcal/mol), Replicase-monomer at NSP-8 binding site (-34.48 kcal/mol), Replicase-dimer interface (-31.29 kcal/mol), Helicase (-30.02 kcal/mol), Papain-like-protease (-28.12 kcal/mol), 2'-O-methyltransferase (-23.17 kcal/mol), Main-protease (-21.63 kcal/mol), Replicase-monomer at dimer interface (-22.04 kcal/mol), RNA-dependent-RNA-polymerase (-19.98 kcal/mol), Nucleocapsid-NTD (-16.92 kcal/mol), and Endoribonuclease (-16.81 kcal/mol). Furthermore, we identify and evaluate the potential human targets of fortunellin and its effect on the SARS-CoV-2 infected tissues, including normal-human-bronchial-epithelium (NHBE) and lung cells and organoids such as pancreatic, colon, liver, and cornea using a network pharmacology approach. Thus, our findings indicate that fortunellin has a dual role; multi-target antiviral activities against SARS-CoV-2 and immunomodulatory capabilities against the host.

Molecular dynamics simulations highlight the altered binding landscape at the spike-ACE2 interface between the Delta and Omicron variants compared to the SARS-CoV-2 original strain

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) B.1.1.529 variant (Omicron), represents a significant deviation in genetic makeup and function compared to previous variants. Following the BA.1 sublineage, the BA.2 and BA.3 Omicron subvariants became dominant, and currently the BA.4 and BA.5, which are quite distinct variants, have emerged. Using molecular dynamics simulations, we investigated the binding characteristics of the Delta and Omicron (BA.1) variants in comparison to wild-type (WT) at the interface of the spike protein receptor binding domain (RBD) and human angiotensin converting enzyme-2 (ACE2) ectodomain. The primary aim was to compare our molecular modelling systems with previously published observations, to determine the robustness of our approach for rapid prediction of emerging future variants. Delta and Omicron were found to bind to ACE2 with similar affinities (-39.4 and -43.3 kcal/mol, respectively) and stronger than WT (-33.5 kcal/mol). In line with previously published observations, the energy contributions of the non-mutated residues at the interface were largely retained between WT and the variants, with F456, F486, and Y489 having the strongest energy contributions to ACE2 binding. Further, residues N440K, Q498R, and N501Y were predicted to be energetically favourable in Omicron. In contrast to Omicron, which had the E484A and K417N mutations, intermolecular bonds were detected for the residue pairs E484:K31 and K417:D30 in WT and Delta, in accordance with previously published findings. Overall, our simplified molecular modelling approach represents a step towards predictive model systems for rapidly analysing arising variants of concern.

GC-MS profiling of Bauhinia variegata major phytoconstituents with computational identification of potential lead inhibitors of SARS-CoV-2 Mpro

Severe acute respiratory syndrome coronavirus 2 was originally identified in Wuhan city of China in December 2019 and it spread rapidly throughout the globe, causing a threat to human life. Since targeted therapies are deficient, scientists all over the world have an opportunity to develop novel drug therapies to combat COVID-19. After the declaration of a global medical emergency, it was established that the Food and Drug Administration (FDA) could permit the use of emergency testing, treatments, and vaccines to decrease suffering, and loss of life, and restore the nation's health and security. The FDA has approved the use of remdesivir and its analogs as an antiviral medication, to treat COVID-19. The primary protease of SARS-CoV-2, which has the potential to regulate coronavirus proliferation, has been a viable target for the discovery of medicines against SARS-CoV-2. The present research deals with the in silico technique to screen phytocompounds from a traditional medicinal plant, Bauhinia variegata for potential inhibitors of the SARS-CoV-2 main protease. Dried leaves of the plant B. variegata were used to prepare aqueous and methanol extract and the constituents were analyzed using the GC-MS technique. A total of 57 compounds were retrieved from the aqueous and methanol extract analysis. Among these, three lead compounds (2,5 dimethyl 1-H Pyrrole, 2,3 diphenyl cyclopropyl methyl phenyl sulphoxide, and Benzonitrile m phenethyl) were shown to have the highest binding affinity (−5.719 to −5.580 kcal/mol) towards SARS-CoV-2 M^pro. The post MD simulation results also revealed the favorable confirmation and stability of the selected lead compounds with M^pro as per trajectory analysis. The Prime MM/GBSA binding free energy supports this finding, the top lead compound 2,3 diphenyl cyclopropyl methyl phenyl sulphoxide showed high binding free energy (−64.377 ± 5.24 kcal/mol) towards M^pro which reflects the binding stability of the molecule with M^pro. The binding free energy of the complexes was strongly influenced by His, Gln, and Glu residues. All of the molecules chosen are found to have strong pharmacokinetic characteristics and show drug-likeness properties. The lead compounds present acute toxicity (LD50) values ranging from 670 mg/kg to 2500 mg/kg; with toxicity classifications of 4 and 5 classes. Thus, these compounds could behave as probable lead candidates for treatment against SARS-CoV-2. However further in vitro and in vivo studies are required for the development of medication against SARS-CoV-2.

Structural basis of Omicron immune evasion: A comparative computational study

BACKGROUND

The vaccines used against SARS-CoV-2 by now have been able to develop some neutralising antibodies in the vaccinated population and their effectiveness has been challenged by the emergence of the new strains with numerous mutations in the spike protein of SARS-CoV-2. Since S protein is the major immunogenic protein of the virus which contains Receptor Binding Domain (RBD) that interacts with the human Angiotensin-Converting Enzyme 2 (ACE2) receptors, any mutations in this region should affect the neutralisation potential of the antibodies leading to the immune evasion. Several variants of concern of the virus have emerged so far, amongst which the most critical are Delta and recently reported Omicron. In this study, we have mapped and reported mutations on the modelled RBD and evaluated binding affinities of various human antibodies with it.

METHOD

Docking and molecular dynamics simulation studies have been used to explore the effect of mutations on the structure of RBD and RBD-antibody interaction.

RESULTS

These analyses show that the mutations mostly at the interface of a nearby region lower the binding affinity of the antibody by ten to forty percent, with a downfall in the number of interactions formed as a whole. It implies the generation of immune escape variants.

CONCLUSIONS

Notable mutations and their effect was characterised that explain the structural basis of antibody efficacy in Delta and a compromised neutralisation effect for the Omicron variant. Thus, our results pave the way for robust vaccine design that can be effective for many variants.

Exploration and validation of Taraxacum mongolicum anti-cancer effect

Taraxacum mongolicum gained a lot of concern and was applied in 93 formulas in China due to its fame as a traditional Chinese medicine. The earliest recorded application of Taraxacum mongolicum was traced back to the Han dynasty. Generations of doctors boosted the usage and enriched the pharmacological mechanism. Clinical application of the Taraxacum mongolicum is flourishing as it treats multiple diseases. This study aims to explore the anti-cancer effect, retrieve the active ingredients and screen the key targets of Taraxacum mongolicum in cancer therapy. We collected and evaluated 10 key active compounds to investigate the anti-cancer effect via 69 significant targets and a variety of biological processes and pathways. Gene Ontology (GO) enrichment analysis uncovered targets associated with protein phosphorylation, cell proliferation and apoptotic processes via regulation of kinases, ATP and enzyme binding activities. Half of the top 20 enriched Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were directly involved in cancer. Based on standard selection criteria, seven hub targets were obtained. These targets functioned through distinct patterns and pathways in realizing the anti-cancer effect. Molecular docking was conducted to validate the potential combination between compounds and hub targets to explore the pharmacological mechanism of key compounds in Taraxacum mongolicum against cancer. In summary, our findings indicate that the famous and widely used Chinese herb, Taraxacum mongolicum, shows good anti-cancer effect through its active compounds, targeted genes, and multiple involved biological processes. The results may provide a theoretical basis for subsequent experimental validation and drug development of Taraxacum mongolicum extract against cancer.

Investigation of the binding and dynamic features of A.30 variant revealed higher binding of RBD for hACE2 and escapes the neutralizing antibody: A molecular simulation approach

With the emergence of Delta and Omicron variants, many other important variants of SARS-CoV-2, which cause Coronavirus disease-2019, including A.30, are reported to increase the concern created by the global pandemic. The A.30 variant, reported in Tanzania and other countries, harbors spike gene mutations that help this strain to bind more robustly and to escape neutralizing antibodies. The present study uses molecular modelling and simulation-based approaches to investigate the key features of this strain that result in greater infectivity. The protein-protein docking results for the spike protein demonstrated that additional interactions, particularly two salt-bridges formed by the mutated residue Lys484, increase binding affinity, while the loss of key residues at the N terminal domain (NTD) result in a change to binding conformation with monoclonal antibodies, thus escaping their neutralizing effects. Moreover, we deeply studied the atomic features of these binding complexes through molecular simulation, which revealed differential dynamics when compared to wild type. Analysis of the binding free energy using MM/GBSA revealed that the total binding free energy (TBE) for the wild type receptor-binding domain (RBD) complex was -58.25 kcal/mol in contrast to the A.30 RBD complex, which reported -65.59 kcal/mol. The higher TBE for the A.30 RBD complex signifies a more robust interaction between A.30 variant RBD with ACE2 than the wild type, allowing the variant to bind and spread more promptly. The BFE for the wild type NTD complex was calculated to be -65.76 kcal/mol, while the A.30 NTD complex was estimated to be -49.35 kcal/mol. This shows the impact of the reported substitutions and deletions in the NTD of A.30 variant, which consequently reduce the binding of mAb, allowing it to evade the immune response of the host. The reported results will aid the development of cross-protective drugs against SARS-CoV-2 and its variants.

Benchmarking the ability of novel compounds to inhibit SARS-CoV-2 main protease using steered molecular dynamics simulations

BACKGROUND

The SARS-CoV-2 main protease (M^pro) is an attractive target in the COVID-19 drug development process. It catalyzes the polyprotein's translation from viral RNA and specifies a particular cleavage site. Due to the absence of identical cleavage specificity in human cell proteases, targeting M^pro with chemical compounds can obstruct the replication of the virus.

METHODS

To explore the potential binding mechanisms of 1,2,3-triazole scaffolds in comparison to co-crystallized inhibitors 11a and 11b towards M^pro, we herein utilized molecular dynamics and enhanced sampling simulation studies.

RESULTS AND CONCLUSION

All the 1,2,3-triazole scaffolds interacted with catalytic residues (Cys145 and His41) and binding pocket residues of M^pro involving Met165, Glu166, Ser144, Gln189, His163, and Met49. Furthermore, the adequate binding free energy and potential mean force of the topmost compound 3h was comparable to the experimental inhibitors 11a and 11b of M^pro. Overall, the current analysis could be beneficial in developing the SARS-CoV-2 M^pro potential inhibitors.

E2UbcH5B-derived peptide ligands target HECT E3-E2 binding site and block the Ub-dependent SARS-CoV-2 egression: A computational study

Homologous to E6AP carboxyl-terminus (HECT)-type E3 ligase performs ubiquitin (Ub)-proteasomal protein degradation via forming a complex with E2∼Ub. Enveloped viruses including SARS-CoV-2 escape from the infected cells by harnessing the E-class vacuolar protein-sorting (ESCRT) machinery and mimic the cellular system through PPAY motif-based linking to HECT Ub ligase activity. In the present study, we have characterized the binding pattern of E2^UbcH5B to HECT domains of NEDD4L, WWP1, WWP2, HECW1, and HECW2 through in silico analysis to isolate the E2^UbcH5B-specific peptide inhibitors that may target SARS-CoV-2 viral egression. Molecular dynamics analysis revealed more opening of E2^UbcH5B-binding pocket upon binding to HECT^NEDD4L, HECT^WWP1, HECT^WWP2, HECT^HECW1, and HECT^HECW2. We observed similar binding pattern for E2^UbcH5B and mentioned HECT domains as previously reported for HECT^NEDD4L where Trp762, Trp709, and Trp657 residues of HECT^NEDD4L, HECT^WWP1, and HECT^WWP2 are involved in making contacts with Ser94 residue of E2^UbcH5B. Similarly, corresponding to HECT^NEDD4L Tyr756 residue, HECT^WWP1, HECT^WWP2, HECT^HECW1, and HECT^HECW2-specific Phe703, Phe651, Phe1387, and Phe1353 residues execute interaction with E2^UbcH5B. Our analysis suggests that corresponding to Cys942 of HECT^NEDD4L, Cys890, Cys838, Cys1574, and Cys1540 residues of HECT^WWP1, HECT^WWP2, HECT^HECW1, and HECT^HECW2, respectively are involved in E2-to-E3 Ub transfer. Furthermore, MM-PBSA free energy calculations revealed favorable energy values for E2^UbcH5B-HECT complexes along with the individual residue contributions. Subsequently, two E2^UbcH5B-derived peptides (His55-Phe69 and Asn81-Ala96) were tested for their binding abilities against HECT domains of NEDD4L, WWP1, WWP2, HECW1, and HECW2. Their binding was validated through substitution of Phe62, Pro65, Ile84, and Cys85 residues into Ala, which revealed an impaired binding, suggesting that the proposed peptide ligands may selectively target E2-HECT binding and Ub-transfer. Collectively, we propose that peptide-driven blocking of E2-to-HECT Ub loading may limit SARS-CoV-2 egression and spread in the host cells.

Computational approach to decode the mechanism of curcuminoids against neuropathic pain

BACKGROUND

Curcumin (CUR), demethoxycurcumin (DMC) and bisdemethoxycurcumin (BDMC) are the main components of turmeric that commonly used to treat neuropathic pain (NP). However, the mechanism of the therapy is not sufficiently clarified. Herein, network pharmacology, molecular docking and molecular dynamics (MD) approaches were used to investigate the mechanism of curcuminoids for NP treatment.

METHODS

Active targets of curcuminoids were obtained from the Swiss Target database, and NP-related targets were retrieved from GeneCards, OMIM, Drugbank and TTD databases. A protein-protein interaction (PPI) network was built to screen the core targets. Furthermore, DAVID was used for GO and KEGG pathway enrichment analyses. Interactions between potential targets and curcuminoids were assessed by molecular docking and the MD simulations were run for 100ns to validate the docking results on the top six complexes.

RESULTS

CUR, DMC, and BDMC had 100, 99 and 100 targets respectively. After overlapping with NP there were 33, 33 and 31 targets respectively. PPI network analysis of TOP 10 core targets, TNF, GSK3β were common targets of curcuminoids. Molecular docking and MD results indicated that curcuminoids bind strongly with the core targets. The GO and KEGG showed that curcuminoids regulated nitrogen metabolism, the serotonergic synapse and ErbB signaling pathway to alleviate NP. Furthermore, specific targets in these three compounds were also analysed at the same time.

CONCLUSIONS

This study systematically explored and compared the anti-NP mechanism of curcuminoids, providing a novel perspective for their utilization.

Computational repurposing approach for targeting the critical spike mutations in B.1.617.2 (delta), AY.1 (delta plus) and C.37 (lambda) SARS-CoV-2 variants using exhaustive structure-based virtual screening, molecular dynamic simulations and MM-PBSA methods

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of the contagious coronavirus disease 2019 (COVID-19) which was first identified in Wuhan, China, in December 2019. Around the world, many researchers focused their research on identifying inhibitors against the druggable SARS-CoV-2 targets. The reported genomic mutations have a direct effect on the receptor-binding domain (RBD), which interacts with host angiotensin-converting enzyme 2 (ACE-2) for viral cell entry. These mutations, some of which are variants of concern (VOC), lead to increased morbidity and mortality rates. The newest variants including B.1.617.2 (Delta), AY.1 (Delta plus), and C.37 (Lambda) were considered in this study. Thus, an exhaustive structure-based virtual screening of a ligand library (in which FDA approved drugs are also present) using the drug-likeness screening, molecular docking, ADMET profiling was performed followed by molecular dynamics (MD) simulation, and Molecular Mechanics-Poisson Boltzmann Surface Area (MM-PBSA) calculation to identify compounds or drugs can be repurposed for inhibiting the wild type, Delta, Delta plus and Lambda variants of RBD of the spike protein. Based on the virtual screening steps, two FDA approved drugs, Atovaquone (atv) and Praziquantel (prz), were selected and repurposed as the best candidates of SARS-CoV-2 RBD inhibitors. Molecular docking results display that both atv and prz contribute in different interaction with binding site residues (Gln493, Asn501 and Gly502 in the hydrogen bond formation, Phe490 and Tyr505 in the π- π stacking and Tyr449, Ser494, and Phe497 in the vdW interactions) in the wild type, Delta, Delta plus and Lambda variants of RBD of the spike protein. MD simulations revealed that among the eight studied complexes, the wild type-atv and Delta-prz complexes have the most structural stability over the simulation time. Furthermore, MM-PBSA calculation showed that in the atv containing complexes, highest binding affinity is related to the wild type-atv complex and in the prz containing complexes, it is related to the Delta-prz complex. The validation of docking results was done by comparing with experimental data (heparin in complex with wild type and Delta variants). Also, comparison of the obtained results with the result of simulation of the k22 with the studied proteins showed that atv and prz are suitable inhibitors for these proteins, especially wild type t and Delta variant, respectively. Thus, we found that atv and prz are the best candidate for inhibition of wild type and Delta variant of the spike protein. Also, atv can be an appropriate inhibitor for the Lambda variant. Obtained in silico results may help the development of new anti-COVID-19 drugs.

MutCov: A pipeline for evaluating the effect of mutations in spike protein on infectivity and antigenicity of SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), causing an outbreak of coronavirus disease 2019 (COVID-19), is a major threat to public health worldwide. Previous studies have shown that the spike protein of SARS-CoV-2 determines viral infectivity and major antigenicity. However, the spike protein has been undergoing various mutations, which bring a great challenge to the prevention and treatment of COVID-19. Here we present the MutCov, a pipeline for evaluating the effect of mutations in spike protein on infectivity and antigenicity of SARS-CoV-2 by calculating the binding free energy between spike protein and angiotensin-converting enzyme 2 (ACE2) or neutralizing monoclonal antibody (mAb). The predicted infectivity and antigenicity were highly consistent with biologically experimental results, and demonstrated that the MutCov achieved good prediction performance. In conclusion, the MutCov is of high importance for systematically evaluating the effect of novel mutations and improving the prevention and treatment of COVID-19. The source code and installation instruction of MutCov are freely available at http://jianglab.org.cn/MutCov.

Statistics and network-based approaches to identify molecular mechanisms that drive the progression of breast cancer

Breast cancer (BC) is one of the most malignant tumors and the leading cause of cancer-related death in women worldwide. So, an in-depth investigation on the molecular mechanisms of BC progression is required for diagnosis, prognosis and therapies. In this study, we identified 127 common differentially expressed genes (cDEGs) between BC and control samples by analyzing five gene expression profiles with NCBI accession numbers GSE139038, GSE62931, GSE45827, GSE42568 and GSE54002, based-on two statistical methods LIMMA and SAM. Then we constructed protein-protein interaction (PPI) network of cDEGs through the STRING database and selected top-ranked 7 cDEGs (BUB1, ASPM, TTK, CCNA2, CENPF, RFC4, and CCNB1) as a set of key genes (KGs) by cytoHubba plugin in Cytoscape. Several BC-causing crucial biological processes, molecular functions, cellular components, and pathways were significantly enriched by the estimated cDEGs including at-least one KGs. The multivariate survival analysis showed that the proposed KGs have a strong prognosis power of BC. Moreover, we detected some transcriptional and post-transcriptional regulators of KGs by their regulatory network analysis. Finally, we suggested KGs-guided three repurposable candidate-drugs (Trametinib, selumetinib, and RDEA119) for BC treatment by using the GSCALite online web tool and validated them through molecular docking analysis, and found their strong binding affinities. Therefore, the findings of this study might be useful resources for BC diagnosis, prognosis and therapies.

A multidrug efflux protein in Mycobacterium tuberculosis; tap as a potential drug target for drug repurposing

Tuberculosis (TB) is a serious communicative disease caused by Mycobacterium tuberculosis. Although there are vaccines and drugs available to treat the disease, they are not efficient, moreover, multidrug-resistant TB (MDR-TB) become a major hurdle in its therapy. These MDR strains utilize the multidrug efflux pump as a decisive weapon to fight against antitubercular drugs. Tap membrane protein was observed as a crucial multidrug efflux pump in M. tuberculosis and its critical implication in MDR-MTB development makes it an effective drug target. In the present study, we have utilized various in silico approaches to predict the applicability of FDA-approved ion channel inhibitors and blockers as therapeutic leads against Tuberculosis by targeting multidrug efflux protein; Tap in MTB. Tap protein structure is predicted by Phyre2 server followed by model refinement, validation, physio-chemical catheterization and target prediction. Further, the interaction between Tap protein and ligands were analysed by molecular docking and MD simulation run of 100 ns. Based on implication and compatibility, 18 FDA-approved ion channel inhibitors and blockers are selected as a ligand against the Tap protein and eventually observed five ligands; Glimepiride, Flecainide, Flupiritine, Nimodipine and Amlodipine as promising compounds which have exhibited the significant stable interaction with Tap protein and are proposed to modulate or interfere with its activity. These compounds illustrated the substantial docking score and total binding enthalpy more than -7 kcal/mol and -42 kcal/mol respectively which implies that the selected FDA-approved compounds can spontaneously interact with the Tap protein to modulate its function. This study proposed Tap protein as a prominent drug target in MTB and investigated compounds that show considerable interaction with the Tap protein as potential therapeutic molecules. These interactions may lead to modulating or inhibit the activity of drug efflux protein thereby making MTB susceptible to antitubercular drugs.

Alleles of unc-33/CRMP exhibit defects during Caenorhabditis elegans epidermal morphogenesis

BACKGROUND

Microtubule-associated proteins regulate the dynamics, organization, and function of microtubules, impacting a number of vital cellular processes. CRMPs have been shown to control microtubule assembly and axon outgrowth during neuronal differentiation. While many microtubule-associated proteins have been linked to roles in cell division and neuronal development, it is still unclear the complement that control the formation of parallel microtubule arrays in epithelial cells.

RESULTS

Here we show through time-lapse DIC microscopy that Caenorhabditis elegans embryos homozygous for the weak loss-of-function allele unc-33(e204) progress more slowly through epidermal morphogenesis, while animals homozygous for strong loss-of-function alleles exhibit more embryonic lethality. Identification of two novel missense mutations in unc-33(e572), Val476Gly and Ser731Thr, lead to computational approaches to determine the potential effects of these changes on UNC-33/CRMP structure. Molecular dynamics simulations show that for Asp389Asn and Arg502His, two other known missense mutations, local changes in protein-protein hydrogen bonding affect the stability of the protein. However, the Val476Gly/Ser731Thr combination does not alter the structure or energetics of UNC-33 drastically when compared to the wild-type protein.

CONCLUSIONS

These results support a novel role for UNC-33/CRMP in C. elegans epidermal development and shed light on how individual amino acid changes cause a loss-of-function in UNC-33. This article is protected by copyright. All rights reserved.

Biological activities of drug inactive ingredients

In a drug formulation (DFM), the major components by mass are not Active Pharmaceutical Ingredient (API) but rather Drug Inactive Ingredients (DIGs). DIGs can reach much higher concentrations than that achieved by API, which raises great concerns about their clinical toxicities. Therefore, the biological activities of DIG on physiologically relevant target are widely demanded by both clinical investigation and pharmaceutical industry. However, such activity data are not available in any existing pharmaceutical knowledge base, and their potentials in predicting the DIG-target interaction have not been evaluated yet. In this study, the comprehensive assessment and analysis on the biological activities of DIGs were therefore conducted. First, the largest number of DIGs and DFMs were systematically curated and confirmed based on all drugs approved by US Food and Drug Administration. Second, comprehensive activities for both DIGs and DFMs were provided for the first time to pharmaceutical community. Third, the biological targets of each DIG and formulation were fully referenced to available databases that described their pharmaceutical/biological characteristics. Finally, a variety of popular artificial intelligence techniques were used to assess the predictive potential of DIGs' activity data, which was the first evaluation on the possibility to predict DIG's activity. As the activities of DIGs are critical for current pharmaceutical studies, this work is expected to have significant implications for the future practice of drug discovery and precision medicine.

Discovery of novel Glutaminase allosteric inhibitors through drug repurposing and comparative MMGB/PBSA and molecular dynamics simulation

GLS1 enzymes (Glutaminase C (GAC) and kidney-type Glutaminase (KGA)) are gaining prominence as a target for tumor treatment including lung, breast, kidney, prostate, and colorectal. To date, several medicinal chemistry studies are being conducted to develop new and effective inhibitors against GLS1 enzymes. Telaglenastat, a drug that targets the allosteric site of GLS1, has undergone clinical trials for the first time for the therapy of solid tumors and hematological malignancies. A comprehensive computational investigation is performed to get insights into the inhibition mechanism of the Telaglenastat. Some novel inhibitors are also proposed against GLS1 enzymes using the drug repurposing approach using 2D-fingerprinting virtual screening method against 2.4 million compounds, application of pharmacokinetics, Molecular Docking, and Molecular Dynamic (MD) Simulations. A TIP3P water box of 10 Å was defined to solvate both enzymes to improve MD simulation reliability. The dynamics results were validated further by the MMGB/PBSA binding free energy method, RDF, and AFD analysis. Results of these computational analysis revealed a stable binding affinity of Telaglenastat, as well as an FDA approved drug Astemizole (IC₅₀ ∼ 0.9 nM) and a novel para position oriented methoxy group containing Chembridge compound (Chem-64284604) that provides an effective inhibitory action against GAC and KGA.

Computational investigation of benzalacetophenone derivatives against SARS-CoV-2 as potential multi-target bioactive compounds

Benzalacetophenones, precursors of flavonoids are aromatic ketones and enones and possess the immunostimulant as well as antiviral activities. Thus, benzalacetophenones were screened against the COVID-19 that could be lethal in patients with compromised immunity. We considered ChEBI recorded benzalacetophenone derivative(s) and evaluated their activity against 3C-like protease (3CL^pro), papain-like protease (PL^pro), and spike protein of SARS-Cov-2 to elucidate their possible role as antiviral agents. The probable targets for each compound were retrieved from DIGEP-Pred at 0.5 pharmacological activity and all the modulated proteins were enriched to identify the probably regulated pathways, biological processes, cellular components, and molecular functions. In addition, molecular docking was performed using AutoDock 4 and the best-identified hits were subjected to all-atom molecular dynamics simulation and binding energy calculations using molecular mechanics Poisson-Boltzmann surface area (MMPBSA). The compound 4-hydroxycordoin showed the highest druglikeness score and regulated nine proteins of which five were down-regulated and four were upregulated. Similarly, enrichment analysis identified the modulation of multiple pathways concerned with the immune system as well as pathways related to infectious and non-infectious diseases. Likewise, 3'-(3-methyl-2-butenyl)-4′-O-β-d-glucopyranosyl-4,2′-dihydroxychalcone with 3CL^pro, 4-hydroxycordoin with PL^pro and mallotophilippen D with spike protein receptor-binding domain showed highest binding affinity, revealed stable interactions during the simulation, and scored binding free energy of −26.09 kcal/mol, −16.28 kcal/mol, and −39.2 kcal/mol, respectively. Predicted anti-SARS-CoV-2 activities of the benzalacetophenones reflected the requirement of wet lab studies to develop novel antiviral candidates.

PFmulDL: a novel strategy enabling multi-class and multi-label protein function annotation by integrating diverse deep learning methods

Bioinformatic annotation of protein function is essential but extremely sophisticated, which asks for extensive efforts to develop effective prediction method. However, the existing methods tend to amplify the representativeness of the families with large number of proteins by misclassifying the proteins in the families with small number of proteins. That is to say, the ability of the existing methods to annotate proteins in the 'rare classes' remains limited. Herein, a new protein function annotation strategy, PFmulDL, integrating multiple deep learning methods, was thus constructed. First, the recurrent neural network was integrated, for the first time, with the convolutional neural network to facilitate the function annotation. Second, a transfer learning method was introduced to the model construction for further improving the prediction performances. Third, based on the latest data of Gene Ontology, the newly constructed model could annotate the largest number of protein families comparing with the existing methods. Finally, this newly constructed model was found capable of significantly elevating the prediction performance for the 'rare classes' without sacrificing that for the 'major classes'. All in all, due to the emerging requirements on improving the prediction performance for the proteins in 'rare classes', this new strategy would become an essential complement to the existing methods for protein function prediction. All the models and source codes are freely available and open to all users at: https://github.com/idrblab/PFmulDL.