Effect of hydrophobic and hydrogen bonding interactions on the potency of ß-alanine analogs of G-protein coupled glucagon receptor inhibitors

Development of artificial photosystems based on designed proteins for mechanistic insights into photosynthesis

This review aims to provide an overview of the progress in protein-based artificial photosystem design and their potential to uncover the underlying principles governing light-harvesting in photosynthesis. While significant advances have been made in this area, a gap persists in reviewing these advances. This review provides a perspective of the field, pinpointing knowledge gaps and unresolved challenges that warrant further inquiry. In particular, it delves into the key considerations when designing photosystems based on the chromophore and protein scaffold characteristics, presents the established strategies for artificial photosystems engineering with their advantages and disadvantages, and underscores the recent breakthroughs in understanding the molecular mechanisms governing light-harvesting, charge separation, and the role of the protein motions in the chromophore's excited state relaxation. By disseminating this knowledge, this article provides a foundational resource for defining the field of bio-hybrid photosystems and aims to inspire the continued exploration of artificial photosystems using protein design.

Discovery of A Novel Series of Quinazoline-Thiazole Hybrids as Potential Antiproliferative and Anti-Angiogenic Agents

Considering the pivotal role of angiogenesis in solid tumor progression, we developed a novel series of quinazoline-thiazole hybrids (SA01-SA07) as antiproliferative and anti-angiogenic agents. Four out of the seven compounds displayed superior antiproliferative activity (IC50 =1.83-4.24 µM) on HepG2 cells compared to sorafenib (IC50 = 6.28 µM). The affinity towards the VEGFR2 kinase domain was assessed through in silico prediction by molecular docking, molecular dynamics studies, and MM-PBSA. The series displayed a high degree of similarity to sorafenib regarding the binding pose within the active site of VEGFR2, with a different orientation of the 4-substituted-thiazole moieties in the allosteric pocket. Molecular dynamics and MM-PBSA evaluations identified SA05 as the hybrid forming the most stable complex with VEGFR2 compared to sorafenib. The impact of the compounds on vascular cell proliferation was assessed on EA.hy926 cells. Six compounds (SA01-SA05, SA07) displayed superior anti-proliferative activity (IC50 = 0.79-5.85 µM) compared to sorafenib (IC50 = 6.62 µM). The toxicity was evaluated on BJ cells. Further studies of the anti-angiogenic effect of the most promising compounds, SA04 and SA05, through the assessment of impact on EA.hy296 motility using a wound healing assay and in ovo potential in a CAM assay compared to sorafenib, led to the confirmation of the anti-angiogenic potential.

Design of new small molecules derived from indolin-2-one as potent TRKs inhibitors using a computer-aided drug design approach

Tropomyosin receptor kinase (TRKs) enzymes are responsible for cancers associated with the neurotrophic tyrosine kinase receptor gene fusion and are identified as effective targets for anticancer drug discovery. A series of small-molecule indolin-2-one derivatives showed remarkable biological activity against TRKs enzymatic activity. These small molecules could have an excellent profile for pharmaceutical application in the treatment of cancers caused by TRKs activity. The aim of this study is to modify the structure of these molecules to obtain new molecules with improved TRK inhibitory activity and pharmacokinetic properties favorable to the design of new drugs. Based on these series, we carried out a 3D-QSAR study. As a result, robust and reliable CoMFA and CoMSIA models are developed and applied to the design of 11 new molecules. These new molecules have a biological activity superior to the most active molecule in the starting series. The eleven designed molecules are screened using drug-likeness, ADMET proprieties, molecular docking, and MM-GBSA filters. The results of this screening identified the T1, T3, and T4 molecules as the best candidates for strong inhibition of TRKs enzymatic activity. In addition, molecular dynamics simulations are performed for TRK free and complexed with ligands T1, T3, and T4 to evaluate the stability of ligand-protein complexes over the simulation time. On the other hand, we proposed experimental synthesis routes for these newly designed molecules. Finally, the designed molecules T1, T2, and T3 have great potential to become reliable candidates for the conception of new drug inhibitors of TRKs.Communicated by Ramaswamy H. Sarma.

Combination of bendamustine-azacitidine against Syk target of breast cancer: an in silico study

Breast cancer (BC) is the most serious and second leading cause of death in women worldwide. When breast cancer is diagnosed and treated early, the chance of long-term survival is up to 90%. On the other hand, 90% of BC patient deaths are due to metastasis and a lack of effective early diagnosis. The existing conventional chemotherapy provides negative feedback due to transportation barriers towards the action sites, multidrug resistance, poor bio-availability, non-specific delivery and systemic side effects on the healthy tissue. Syk protein Kinase has been reported in BC, as a tumor modulator, providing a pro-survival signal and also by restricting epithelial-mesenchymal transition, enhancing cell-cell interactions and inhibiting migration. In the present study, we explored the possibility of targeting BC by attenuating Syk protein Kinase. Hence, we have conjugated the hydrophobic Bendamustine (BEN) and hydrophilic Azacitidine (AZA) anticancer drugs to evaluate their efficacy against BC. The native drugs (BEN and AZA) and designed drug-drug conjugate (BEN-AZA) were docked with Syk protein. Then, the docked complex was performed for Binding Free Energy and Molecular Dynamics Simulations. Furthermore, DFT and ADME properties were carried out. The results revealed that the designed drug-drug conjugate has a better docking score, ΔGbind and admirable stability throughout the simulation when compared with native drugs. In DFT and ADME analyses, the designed drug-drug conjugate has shown good stereo electronic features and pharmaceutical relevant parameters than that of native drugs. The overall results suggested that the designed drug-drug conjugate may be a suitable candidate for BC treatment.Communicated by Ramaswamy H. Sarma.

Molecular mechanism of inhibition of COVID-19 main protease by β-adrenoceptor agonists and adenosine deaminase inhibitors using in silico methods

Novel coronavirus (COVID-19) responsible for viral pneumonia which emerged in late 2019 has badly affected the world. No clinically proven drugs are available yet as the targeted therapeutic agents for the treatment of this disease. The viral main protease which helps in replication and transcription inside the host can be an effective drug target. In the present study, we aimed to discover the potential of β-adrenoceptor agonists and adenosine deaminase inhibitors which are used in asthma and cancer/inflammatory disorders, respectively, as repurposing drugs against protease inhibitor by ligand-based and structure-based virtual screening using COVID-19 protease-N3 complex. The AARRR pharmacophore model was used to screen a set of 22,621 molecules to obtain hits, which were subjected to high-throughput virtual screening. Extra precision docking identified four top-scored molecules such as +/--fenoterol, FR236913 and FR230513 with lower binding energy from both categories. Docking identified three major hydrogen bonds with Gly143, Glu166 and Gln189 residues. 100 ns MD simulation was performed for four top-scored molecules to analyze the stability, molecular mechanism and energy requirements. MM/PBSA energy calculation suggested that van der Waals and electrostatic energy components are the main reasons for the stability of complexes. Water-mediated hydrogen bonds between protein-ligand and flexibility of the ligand are found to be responsible for providing extra stability to the complexes. The insights gained from this combinatorial approach can be used to design more potent and bio-available protease inhibitors against novel coronavirus.Communicated by Ramaswamy H. Sarma.

Structure Prediction and Potential Inhibitors Docking of Enterovirus 2C Proteins

Human enterovirus infections are mostly asymptomatic and occasionally could be severe and life-threatening. The conserved non-structural 2C from enteroviruses protein is a promising target in antiviral therapies against human enteroviruses. Understanding of 2C-drug interactions is crucial for developing the potential antiviral agents. While functions of enterovirus 2C proteins have been widely studied, three-dimensional structure information of 2C is limited. In this study, the structures of 2C proteins from 20 enteroviruses were simulated and reconstructed using I-TASSER programs. Subsequent docking studies of the known 22 antiviral inhibitors for 2C proteins were performed to uncover the inhibitor-binding characteristics of 2C. Among the potential inhibitors, the compound hydantoin exhibited the highest broad-spectrum antiviral activities with binding to 2C protein. The anti-enteroviral activity of GuaHCL, compound 19b, R523062, compound 12a, compound 12b, quinoline analogs 12a, compound 19d, N⁶-benzyladenosine, dibucaine derivatives 6i, TBZE-029, fluoxetine analogs 2b, dibucaine, 2-(α-hydroxybenzyl)-benzimidazole (HBB), metrifudil, pirlindole, MRL-1237, quinoline analogs 10a, zuclopenthixol, fluoxetine, fluoxetine HCl, and quinoline analogs 12c showed a trend of gradual decrease. In addition, the free energy with 22 compounds binding to EV 2C ranged from −0.35 to −88.18 kcal/mol. Our in silico studies will provide important information for the development of pan-enterovirus antiviral agents based on 2C.

Molecular dynamics simulations, docking and MMGBSA studies of newly designed peptide-conjugated glucosyloxy stilbene derivatives with tumor cell receptors

In this work, for the first time, we designed derivatives of beta-D-glucosyloxy-3-hydroxy-trans-stiblene-2-carboxylic acid (GHS), by conjugating GHS with tumor targeting peptides RPARPAR and GGKRPAR to target over-expressed receptors in tumor cells. The sequences RPARPAR and GGKRPAR are known to target the neuropilin1 (NRP1) receptor due to the C-terminal Arg domain; however, their effectiveness has never been examined with other commonly over-expressed receptors in tumor cells, particularly of chronic lymphocytic leukemia that include integrin α1β1 and CD22. By conjugating these peptides with GHS, which is known for its inherent anti-cancer properties, the goal is to further enhance tumor cell targeting by developing compounds that can target multiple receptors. The physicochemical properties of the conjugates and individual peptides were analyzed using Turbomole and COSMOthermX20 in order to determine their hydrogen bond accepting and donating capabilities. The web server POCASA was used in order to determine the surface cavities and binding pockets of the three receptors. To explore the binding affinities, we conducted molecular docking studies with the peptides and the conjugates with each of the receptors. After molecular docking, the complexes were analyzed using Protein-Ligand Interaction Profiler to determine the types of interactions involved. Molecular dynamics simulation studies were conducted to explore the stability of the receptor-ligand complexes. Our results indicated that in most cases the conjugates showed higher binding and stability with the receptors. Additionally, highly stable complexes of conjugates were obtained with CD22, NRP1 and in most cases with the integrin α1β1 receptor as well. The binding energies were calculated for each of the receptor ligand complexes through trajectory analysis using MMGBSA studies. SwissADME studies revealed that the compounds showed low GI absorption and were not found to be CYP inhibitors and had bioavailability score that would allow them to be considered as potential drug candidates. Overall, our results for the first time show that the designed conjugates can target multiple over-expressed receptors in tumor cells and may be potentially developed as future therapeutics for targeting tumor cells.

In Silico Studies of Tumor Targeted Peptide-Conjugated Natural Products for Targeting Over-Expressed Receptors in Breast Cancer Cells Using Molecular Docking, Molecular Dynamics and MMGBSA Calculations

In this work, in silico studies were carried out for the design of diterpene and polyphenol-peptide conjugates to potentially target over-expressed breast tumor cell receptors. Four point mutations were induced into the known tumor-targeting peptide sequence YHWYGYTPQN at positions 1, 2, 8 and 10, resulting in four mutated peptides. Each peptide was separately conjugated with either chlorogenate, carnosate, gallate, or rosmarinate given their known anti-tumor activities, creating dual targeting compounds. Molecular docking studies were conducted with the epidermal growth factor receptor (EGFR), to which the original peptide sequence is known to bind, as well as the estrogen receptor (ERα) and peroxisome proliferator-activated receptor (PPARα) using both Autodock Vina and FireDock. Based on docking results, peptide conjugates and peptides were selected and subjected to molecular dynamics simulations. MMGBSA calculations were used to further probe the binding energies. ADME studies revealed that the compounds were not CYP substrates, though most were Pgp substrates. Additionally, most of the peptides and conjugates showed MDCK permeability. Our results indicated that several of the peptide conjugates enhanced binding interactions with the receptors and resulted in stable receptor-ligand complexes; Furthermore, they may successfully target ERα and PPARα in addition to EGFR and may be further explored for synthesis and biological studies for therapeutic applications.

Ligand-Receptor Interactions and Machine Learning in GCGR and GLP-1R Drug Discovery

The large amount of data that has been collected so far for G protein-coupled receptors requires machine learning (ML) approaches to fully exploit its potential. Our previous ML model based on gradient boosting used for prediction of drug affinity and selectivity for a receptor subtype was compared with explicit information on ligand-receptor interactions from induced-fit docking. Both methods have proved their usefulness in drug response predictions. Yet, their successful combination still requires allosteric/orthosteric assignment of ligands from datasets. Our ligand datasets included activities of two members of the secretin receptor family: GCGR and GLP-1R. Simultaneous activation of two or three receptors of this family by dual or triple agonists is not a typical kind of information included in compound databases. A precise allosteric/orthosteric ligand assignment requires a continuous update based on new structural and biological data. This data incompleteness remains the main obstacle for current ML methods applied to class B GPCR drug discovery. Even so, for these two class B receptors, our ligand-based ML model demonstrated high accuracy (5-fold cross-validation Q² > 0.63 and Q² > 0.67 for GLP-1R and GCGR, respectively). In addition, we performed a ligand annotation using recent cryogenic-electron microscopy (cryo-EM) and X-ray crystallographic data on small-molecule complexes of GCGR and GLP-1R. As a result, we assigned GLP-1R and GCGR actives deposited in ChEMBL to four small-molecule binding sites occupied by positive and negative allosteric modulators and a full agonist. Annotated compounds were added to our recently released repository of GPCR data.

Theoretical insights into molecular mechanism and energy criteria of PARP-2 enzyme inhibition by benzimidazole analogues

The emergence of poly (ADP-ribose) polymerase (PARP) inhibitors targeting a class of PARP enzymes has gained a great interest in cancer therapy. Majority of the PARP inhibitors are not isoform-selective which may cause unwanted off-target effects. In the present study, we explore the molecular mechanism and energy requirements for PARP-2 inhibition. This involves docking studies, frontier molecular orbital analysis, 500 ns molecular dynamics simulation (MD), binding free energy analysis and principal component analysis. The results clearly suggest the importance of hydrogen bonding (Gly429, Gln332, Ser470, Tyr455) and π-π stacking interactions (His428, Tyr455, Tyr462, Phe463, Tyr473) between residues and the inhibitor. Presence of lowest unoccupied molecular orbitals favors π-π stacking interactions and highest occupied molecular orbital orbital favors hydrogen-bonding interactions in the ligands. The stability of most active/PARP-2 complex is confirmed by hydrogen bonding and π-π stacking interaction parameters. Molecular-mechanics Poisson-Boltzmann surface area energy calculations showed that van der Waals and nonpolar solvation energy terms are crucial components for the stable binding of the ligands. Per residue analysis showed that tyrosine, histidine, and phenyl alanine residues are responsible for hydrophobic interactions with the ligands. Four new inhibitors are designed based on this study and the stability of PARP-2/inhibitor complex is validated by MD, density functional theory studies, and ADME/toxicity properties. Information from the present study can serve as a basis for designing new isoform-selective PARP-2 inhibitors.

Identification of polyphenols from Broussonetia papyrifera as SARS CoV-2 main protease inhibitors using in silico docking and molecular dynamics simulation approaches

The current COVID-19 pandemic is caused by SARS CoV-2. To date, ∼463,000 people died worldwide due to this disease. Several attempts have been taken in search of effective drugs to control the spread of SARS CoV-2 infection. The main protease (Mpro) from SARS CoV-2 plays a vital role in viral replication and thus serves as an important drug target. This Mpro shares a high degree of sequence similarity (>96%) with the same protease from SARS CoV-1 and MERS. It was already reported that Broussonetia papyrifera polyphenols efficiently inhibit the catalytic activity of SARS CoV-1 and MERS Mpro. But whether these polyphenols exhibit any inhibitory effect on SARS CoV-2 Mpro is far from clear. To understand this fact, here we have adopted computational approaches. Polyphenols having proper drug-likeness properties and two repurposed drugs (lopinavir and darunavir; having binding affinity −7.3 to −7.4 kcal/mol) were docked against SARS CoV-2 Mpro to study their binding properties. Only six polyphenols (broussochalcone A, papyriflavonol A, 3'-(3-methylbut-2-enyl)-3',4',7-trihydroxyflavane, broussoflavan A, kazinol F and kazinol J) had interaction with both the catalytic residues (His41 and Cys145) of Mpro and exhibited good binding affinity (−7.6 to −8.2 kcal/mol). Molecular dynamic simulations (100 ns) revealed that all Mpro-polyphenol complexes are more stable, conformationally less fluctuated; slightly less compact and marginally expanded than Mpro-darunavir/lopinavir complex. Even the number of intermolecular H-bond and MM-GBSA analysis suggested that these six polyphenols are more potent Mpro inhibitors than the two repurposed drugs (lopinavir and darunavir) and may serve as promising anti-COVID-19 drugs.

Communicated by Ramaswamy H. Sarma

Computational investigation of potential inhibitors of novel coronavirus 2019 through structure-based virtual screening, molecular dynamics and density functional theory studies

Despite the intensive research efforts towards antiviral drug against COVID-19, no potential drug or vaccines has not yet discovered. Initially, the binding site of COVID-19 main protease was predicted which located between regions 2 and 3. Structure-based virtual screening was performed through a hierarchal mode of elimination technique after generating a grid box. This led to the identification of five top hit molecules that were selected on the basis of docking score and visualization of non-bonding interactions. The docking results revealed that the hydrogen bonding and hydrophobic interactions are the major contributing factors in the stabilization of complexes. The docking scores were found between −7.524 and −6.711 kcal/mol indicating strong ligand-protein interactions. Amino acid residues Phe140, Leu141, Gly143, Asn142, Thr26, Glu166 and Thr190 (hydrogen bonding interactions) and Phe140, Cys145, Cys44, Met49, Leu167, Pro168, Met165, Val42, Leu27 and Ala191 (hydrophobic interactions) formed the binding pocket of COVID-19 main protease. From identified hits, ZINC13144609 and ZINC01581128 were selected for atomistic MD simulation and density functional theory calculations. MD simulation results confirm that the protein interacting with both hit molecules is stabilized in the chosen POPC lipid bilayer membrane. The presence of lowest unoccupied molecular orbital (LUMO) and highest occupied molecular orbital (HOMO) in the hydrophobic region of the hit molecules leads to favorable ligand-protein contacts. The calculated pharmacokinetic descriptors were found to be in their acceptable range and therefore confirming their drug-like properties. Hence, the present investigation can serve as the basis for designing and developing COVID-19 inhibitors.

Communicated by Ramaswamy H. Sarma

Evaluation of green tea polyphenols as novel corona virus (SARS CoV-2) main protease (Mpro) inhibitors - an in silico docking and molecular dynamics simulation study

Coronavirus disease 2019 (COVID-19) is a viral respiratory disease which caused global health emergency and announced as pandemic disease by World Health Organization. Lack of specific drug molecules or treatment strategy against this disease makes it more devastating. Thus, there is an urgent need of effective drug molecules to fight against COVID-19. The main protease (Mpro) of SARS CoV-2, a key component of this viral replication, is considered as a prime target for anti-COVID-19 drug development. In order to find potent Mpro inhibitors, we have selected eight polyphenols from green tea, as these are already known to exert antiviral activity against many RNA viruses. We have elucidated the binding affinities and binding modes between these polyphenols including a well-known Mpro inhibitor N3 (having binding affinity -7.0 kcal/mol) and Mpro using molecular docking studies. All eight polyphenols exhibit good binding affinity toward Mpro (-7.1 to -9.0 kcal/mol). However, only three polyphenols (epigallocatechin gallate, epicatechingallate and gallocatechin-3-gallate) interact strongly with one or both catalytic residues (His41 and Cys145) of Mpro. Molecular dynamics simulations (100 ns) on these three Mpro-polyphenol systems further reveal that these complexes are highly stable, experience less conformational fluctuations and share similar degree of compactness. Estimation of total number of intermolecular H-bond and MM-GBSA analysis affirm the stability of these three Mpro-polyphenol complexes. Pharmacokinetic analysis additionally suggested that these polyphenols possess favorable drug-likeness characteristics. Altogether, our study shows that these three polyphenols can be used as potential inhibitors against SARS CoV-2 Mpro and are promising drug candidates for COVID-19 treatment.