Molecular docking and molecular dynamics simulation studies of GPR40 receptor-agonist interactions

Structure-Based Virtual Screening of Helicobacter pylori SecA Inhibitors

The human bacterial pathogen Helicobacter pylori causes a range of gastric diseases. The killing rate of Helicobacter pylori is declining year by year because of high antibiotics resistance. It is urgent to develop new target and novel anti- Helicobacter pylori drugs. As an "energy pump" for bacterial cells, SecA is essential for bacterial growth and drives bacterial protein transmembrane transport, moreover SecA is absent in mammals, all of which nominate SecA as an attractive antimicrobial target. Here, we provided a structure-based virtual screening method to screen the 3D-diversity natural-product-like screening library against SecA for novel anti- Helicobacter pylori inhibitors with novel scaffolds. In this study, homology modeling was used to construct the three-dimensional structure of Helicobacter pylori SecA. Two rounds of molecular docking were then used to find new small-molecule inhibitors of SecA, identifying six lead candidates that maintained key interactions with the binding pocket. After that, molecular dynamics simulations were used to explore more accurate ligand-receptor binding modes in states close to natural conditions. Encouragingly, all six compounds were relatively stable during the simulation. Apart from that the binding free energy calculation based on MM/PBSA demonstrated favorable results of < -13.642 kcal/mol. Finally, ADME-T analysis indicated that these compounds were also sufficiently druggable. All six compounds can be well combined with the crystal structure, which further facilitate the development of SecA inhibitors and lead compounds against Helicobacter pylori.

Pharmacoinformatics-Based Approach for Uncovering the Quorum-Quenching Activity of Phytocompounds against the Oral Pathogen, Streptococcus mutans

Streptococcus mutans, a gram-positive oral pathogen, is the primary causative agent of dental caries. Biofilm formation, a critical characteristic of S. mutans, is regulated by quorum sensing (QS). This study aimed to utilize pharmacoinformatics techniques to screen and identify effective phytochemicals that can target specific proteins involved in the quorum sensing pathway of S. mutans. A computational approach involving homology modeling, model validation, molecular docking, and molecular dynamics (MD) simulation was employed. The 3D structures of the quorum sensing target proteins, namely SecA, SMU1784c, OppC, YidC2, CiaR, SpaR, and LepC, were modeled using SWISS-MODEL and validated using a Ramachandran plot. Metabolites from Azadirachta indica (Neem), Morinda citrifolia (Noni), and Salvadora persica (Miswak) were docked against these proteins using AutoDockTools. MD simulations were conducted to assess stable interactions between the highest-scoring ligands and the target proteins. Additionally, the ADMET properties of the ligands were evaluated using SwissADME and pkCSM tools. The results demonstrated that campesterol, meliantrol, stigmasterol, isofucosterol, and ursolic acid exhibited the strongest binding affinity for CiaR, LepC, OppC, SpaR, and Yidc2, respectively. Furthermore, citrostadienol showed the highest binding affinity for both SMU1784c and SecA. Notably, specific amino acid residues, including ASP86, ARG182, ILE179, GLU143, ASP237, PRO101, and VAL84 from CiaR, LepC, OppC, SecA, SMU1784c, SpaR, and YidC2, respectively, exhibited significant interactions with their respective ligands. While the docking study indicated favorable binding energies, the MD simulations and ADMET studies underscored the substantial binding affinity and stability of the ligands with the target proteins. However, further in vitro studies are necessary to validate the efficacy of these top hits against S. mutans.

Determination of Hybrid TSPO Ligands with Minimal Impact of SNP (rs6971) through Molecular Docking and MD Simulation Study

Background:

In an endeavor to ascertain high-affinity TSPO ligands with minimal single nucleotide polymorphism (SNP), six hybrid molecules have been identified as new leads for future inflammation PET imaging.

Objective:

Genesis for chemical design was encouraged from structural families of well-known ligands FEBMP and PBR28/ DAA1106 that have demonstrated remarkable TSPO binding characteristics.

Methods:

All proposed hybrid ligands 1-6 are subjected to molecular docking and simulation studies with wild and mutant protein to study their interactions, binding, consistency of active conformations and are correlated with well-established TSPO ligands.

Results:

Each hybrid ligand demonstrate better docking score > -11.00 kcal/mol with TSPO with respect to gold standard PK11195, i.e., -11.00 kcal/mol for 4UC3 and -12.94 kcal/mol for 4UC1. On comparison with FEBMP and GE-180 (-12.57, -7.24 kcal/mol for 4UC3 and -14.10, -11.32 kcal/mol for 4UC1), ligand 3 demonstrates maximum docking energy (> -15.50 kcal/mol) with minimum SNP (0.26 kcal/mol).

Discussion:

Presence of strong hydrogen bond Arg148-3.27Å (4UC1) and Trp50-2.43Å, Asp28- 2.57Å (4UC3) apart from short-range interactions, including π–π interactions with the aromatic residues, such as (Trp39, Phe46, Trp135) and (Trp39, Trp108), attributes towards its strong binding.

Conclusion:

Utilizing the results of binding energy, we concluded stable complex formation of these hybrid ligands that could bind to TSPO with the least effect of SNP with similar interactions to known ligands. Overall, ligand 3 stands out as the best ligand having insignificant deviations per residue of protein that can be further explored and assessed in detail for future inflammation PET application after subsequent detailed biological evaluation.

A comparative study of the interaction of naringenin with lysozyme by multi-spectroscopic methods, activity comparisons, and molecular modeling procedures

The present study applied steady-state fluorescence, UV-Vis spectrophotometry, molecular docking studies, and circular dichroism (CD) to investigate the interaction of naringenin with lysozyme in an aqueous medium. The UV-Vis measurement indicated the changes in lysozyme secondary and tertiary structure change as a function of the concentration of naringenin. Naringenin could be used to turn the static quenching mechanism into the intrinsic fluorescence of lysozyme. The negative amount of Gibbs free energy (ΔG°) suggested that the binding operation was spontaneous. Fluorescence studies also demonstrated the changes occurring in the Trp microenvironment upon the concatenation into lysozyme. Analysis of thermodynamic parameters also revealed that hydrophobic forces played a fundamental role in determining the complex stability; this was consistent with the previous modeling studies. Circular dichroism also suggested that the alpha-helicity of lysozyme was enhanced as ligand was bound. Naringenin inhibited lysozyme enzymatic activity, displaying its affinity with the lysozyme active site. Further, molecular docking studies demonstrated that naringenin could bind to both residues essential for catalytic activity in the proximity of Trp 62 and Trp 63.

Advances in computer-aided drug design for type 2 diabetes

INTRODUCTION

The number of diabetic patients is increasing, posing a heavy social and economic burden worldwide. Traditional drug development technology is time-consuming and costly, and the emergence of computer-aided drug design (CADD) has changed this situation. This study reviews the applications of CADD in diabetic drug designing.

AREAS COVERED

In this article, the authors focus on the advance in CADD in diabetic drug design by elaborating the discovery, including peroxisome proliferator-activated receptor (PPAR), G protein-coupled receptor 40 (GPR40), dipeptidyl peptidase-IV (DDP-IV), protein tyrosine phosphatase 1B (PTP1B), sodium-dependent glucose transporter 2 (SGLT-2), and glucokinase (GK). Some drug discovery of these targets is related to CADD strategies.

EXPERT OPINION

There is no doubt that CADD has contributed to the discovery of novel anti-diabetic agents. However, there are still many limitations and challenges, such as lack of co-crystal complex, dynamic simulations, water, and metal ion treatment. In the near future, artificial intelligence (AI) may be a promising strategy to accelerate drug discovery and reduce costs by identifying candidates. Moreover, AlphaFold, a deep learning model that predicts the 3D structure of proteins, represents a considerable advancement in the structural prediction of proteins, especially in the absence of homologous templates for protein structures.

Ultrahigh Throughput Protein-Ligand Docking with Deep Learning

Ultrahigh-throughput virtual screening (uHTVS) is an emerging field linking together classical docking techniques with high-throughput AI methods. We outline mechanistic docking models' goals and successes. We present different AI accelerated workflows for uHTVS, mainly through surrogate docking models. We showcase a novel feature representation technique, molecular depictions (images), as a surrogate model for docking. Along with a discussion on analyzing screens using regression enrichment surfaces at the tens of billion scale, we outline a future for uHTVS screening pipelines with deep learning.

Current trends in computer aided drug design and a highlight of drugs discovered via computational techniques: A review

Computer-aided drug design (CADD) is one of the pivotal approaches to contemporary pre-clinical drug discovery, and various computational techniques and software programs are typically used in combination, in a bid to achieve the desired outcome. Several approved drugs have been developed with the aid of CADD. On SciFinder®, we evaluated more than 600 publications through systematic searching and refining, using the terms, virtual screening; software methods; computational studies and publication year, in order to obtain data concerning particular aspects of CADD. The primary focus of this review was on the databases screened, virtual screening and/or molecular docking software program used. Furthermore, we evaluated the studies that subsequently performed molecular dynamics (MD) simulations and we reviewed the software programs applied, the application of density functional theory (DFT) calculations and experimental assays. To represent the latest trends, the most recent data obtained was between 2015 and 2020, consequently the most frequently employed techniques and software programs were recorded. Among these, the ZINC database was the most widely preferred with an average use of 31.2%. Structure-based virtual screening (SBVS) was the most prominently used type of virtual screening and it accounted for an average of 57.6%, with AutoDock being the preferred virtual screening/molecular docking program with 41.8% usage. Following the screening process, 38.5% of the studies performed MD simulations to complement the virtual screening and GROMACS with 39.3% usage, was the popular MD software program. Among the computational techniques, DFT was the least applied whereby it only accounts for 0.02% average use. An average of 36.5% of the studies included reports on experimental evaluations following virtual screening. Ultimately, since the inception and application of CADD in pre-clinical drug discovery, more than 70 approved drugs have been discovered, and this number is steadily increasing over time.

Antiviral effects of probiotic metabolites on COVID-19

SARS coronavirus (COVID-19) is a real health challenge of the 21st century for scientists, health workers, politicians, and all humans that has severe cause epidemic worldwide. The virus exerts its pathogenic activity through by mechanism and gains the entry via spike proteins (S) and Angiotensin-Converting Enzyme 2 (ACE2) receptor proteins on host cells. The present work is an effort for a computational target to block the residual binding protein (RBP) on spike proteins (S), Angiotensin-Converting Enzyme 2 (ACE2) receptor proteins by probiotics namely Plantaricin BN, Plantaricin JLA-9, Plantaricin W, Plantaricin D along with RNA-dependent RNA polymerase (RdRp). Docking studies were designed in order to obtain the binding energies for Plantaricin metabolites. The binding energies for Plantaricin W were -14.64, -11.1 and -12.68 for polymerase, RBD and ACE2 respectively comparatively very high with other compounds. Plantaricin W, D, and JLA-9 were able to block the residues (THR556, ALA558) surrounding the deep grove catalytic site (VAL557) of RdRp making them more therapeutically active for COVID-19. Molecular dynamics studies further strengthen stability of the complexes of plantaricin w and SARS-CoV-2 RdRp enzyme, RBD of spike protein, and human ACE2 receptor. The present study present multi-way options either by blocking RBD on S proteins or interaction of S protein with ACE2 receptor proteins or inhibiting RdRp to counter any effect of COVID-19 by Plantaricin molecules paving a way that can be useful in the treatment of COVID-19 until some better option will be available.Communicated by Ramaswamy H. Sarma.

Recent Updates on Free Fatty Acid Receptor 1 (GPR-40) Agonists for the Treatment of Type 2 Diabetes Mellitus

BACKGROUND

The global incidence of type 2 diabetes mellitus (T2DM) has enthused the development of new antidiabetic targets with low toxicity and long-term stability. In this respect, free fatty acid receptor 1 (FFAR1), which is also recognized as a G protein-coupled receptor 40 (GPR40), is a novel target for the treatment of T2DM. FFAR1/GPR40 has a high level of expression in β-cells of the pancreas, and the requirement of glucose for stimulating insulin release results in immense stimulation to utilise this target in the medication of T2DM.

METHODS

The data used for this review is based on the search of several scienctific databases as well as various patent databases. The main search terms used were free fatty acid receptor 1, FFAR1, FFAR1 agonists, diabetes mellitus, G protein-coupled receptor 40 (GPR40), GPR40 agonists, GPR40 ligands, type 2 diabetes mellitus and T2DM.

RESULTS

The present review article gives a brief overview of FFAR1, its role in T2DM, recent developments in small molecule FFAR1 (GPR40) agonists reported till now, compounds of natural/plant origin, recent patents published in the last few years, mechanism of FFAR1 activation by the agonists, and clinical status of the FFAR1/GPR40 agonists.

CONCLUSION

The agonists of FFAR1/GRP40 showed considerable potential for the therapeutic control of T2DM. Most of the small molecule FFAR1/GPR40 agonists developed were aryl alkanoic acid derivatives (such as phenylpropionic acids, phenylacetic acids, phenoxyacetic acids, and benzofuran acetic acid derivatives) and thiazolidinediones. Some natural/plant-derived compounds, including fatty acids, sesquiterpenes, phenolic compounds, anthocyanins, isoquinoline, and indole alkaloids, were also reported as potent FFAR1 agonists. The clinical investigations of the FFAR1 agonists demonstrated their probable role in the improvement of glucose control. Though, there are some problems still to be resolved in this field as some FFAR1 agonists terminated in the late phase of clinical studies due to "hepatotoxicity." Currently, PBI-4050 is under clinical investigation by Prometic. Further investigation of pharmacophore scaffolds for FFAR1 full agonists as well as multitargeted modulators and corresponding clinical investigations will be anticipated, which can open up new directions in this area.

A Multi-target Drug Designing for BTK, MMP9, Proteasome and TAK1 for the Clinical Treatment of Mantle Cell Lymphoma

BACKGROUND

Mantle cell lymphoma (MCL) is a type of non-Hodgkin lymphoma characterized by the mutation and overexpression of the cyclin D1 protein by the reciprocal chromosomal translocation t(11;14)(q13:q32).

AIM

The present study aims to identify potential inhibition of MMP9, Proteasome, BTK, and TAK1 and determine the most suitable and effective protein target for the MCL.

METHODOLOGY

Nine known inhibitors for MMP9, 24 for proteasome, 15 for BTK and 14 for TAK1 were screened. SB-3CT (PubChem ID: 9883002), oprozomib (PubChem ID: 25067547), zanubrutinib (PubChem ID: 135565884) and TAK1 inhibitor (PubChem ID: 66760355) were recognized as drugs with high binding capacity with their respective protein receptors. 41, 72, 102 and 3 virtual screened compounds were obtained after the similarity search with compound (PubChem ID:102173753), PubChem compound SCHEMBL15569297 (PubChem ID:72374403), PubChem compound SCHEMBL17075298 (PubChem ID:136970120) and compound CID: 71814473 with best virtual screened compounds.

RESULT

MMP9 inhibitors show commendable affinity and good interaction profile of compound holding PubChem ID:102173753 over the most effective established inhibitor SB-3CT. The pharmacophore study of the best virtual screened compound reveals its high efficacy based on various interactions. The virtual screened compound's better affinity with the target MMP9 protein was deduced using toxicity and integration profile studies.

CONCLUSION

Based on the ADMET profile, the compound (PubChem ID: 102173753) could be a potent drug for MCL treatment. Similar to the established SB-3CT, the compound was non-toxic with LD50 values for both the compounds lying in the same range.

Ligand and structure-based computational designing of multi-target molecules directing FFAR-1, FFAR-4 and PPAR-G as modulators of insulin receptor activity

Multi-agent therapies are an important treatment modality in many diseases based on the assumption that combining agents may result in increased therapeutic benefit by overcoming the mechanism of resistance and providing superior efficiency. Extensively validated 3D pharmacophore models for free fatty acid receptor-1 (FFAR-1), free fatty acid receptor-4 (FFAR-4), and peroxisome proliferator-activated receptor-G (PPAR-G) was developed. The pharmacophore model for FFAR-1 (r² = 0.98, q² = 0.90) and PPAR-G (r² = 0.89, q² = 0.88) suggested that one hydrogen bond acceptor, one hydrogen bond donor, three aromatic rings, and two hydrophobic groups arranged in 3D space are essential for the binding affinity of FFAR-1 and PPAR-G inhibitors. Similarly, the pharmacophore model for FFAR-4 (r² = 0.92, q² = 0.87) suggested that the presence of a hydrogen bond acceptor, one negative atom, two aromatic rings, and three hydrophobic groups plays a vital role in the binding of an inhibitor of FFAR-4. These pharmacophore models allowed searches for novel FFAR-1, PPAR-G, and FFAR-4 triple inhibitors from multi-conformer 3D databases (Asinex). Finally, the twenty-five best hits were selected for molecular docking, to study the interaction of their complexes with all the proteins and final binding orientations of these molecules. After molecular docking, ten hits have been predicted to possess good binding affinity as per the Molecular Mechanics Generalized Born Surface Area (MM-GBSA) calculation for FFAR-1, FFAR-4, and PPAR-G which can be further investigated for its experimental in-vitro/in-vivo anti-diabetic activities.Communicated by Ramaswamy H. Sarma.

Shape-based Machine Learning Models for the Potential Novel COVID-19 Protease Inhibitors Assisted by Molecular Dynamics Simulation

BACKGROUND

The vast geographical expansion of novel coronavirus and an increasing number of COVID-19 affected cases have overwhelmed health and public health services. Artificial Intelligence (AI) and Machine Learning (ML) algorithms have extended their major role in tracking disease patterns, and in identifying possible treatments.

OBJECTIVE

This study aims to identify potential COVID-19 protease inhibitors through shape-based Machine Learning assisted by Molecular Docking and Molecular Dynamics simulations.

METHODS

31 Repurposed compounds have been selected targeting the main coronavirus protease (6LU7) and a machine learning approach was employed to generate shape-based molecules starting from the 3D shape to the pharmacophoric features of their seed compound. Ligand-Receptor Docking was performed with Optimized Potential for Liquid Simulations (OPLS) algorithms to identify highaffinity compounds from the list of selected candidates for 6LU7, which were subjected to Molecular Dynamic Simulations followed by ADMET studies and other analyses.

RESULTS

Shape-based Machine learning reported remdesivir, valrubicin, aprepitant, and fulvestrant as the best therapeutic agents with the highest affinity for the target protein. Among the best shape-based compounds, a novel compound identified was not indexed in any chemical databases (PubChem, Zinc, or ChEMBL). Hence, the novel compound was named 'nCorv-EMBS'. Further, toxicity analysis showed nCorv-EMBS to be suitable for further consideration as the main protease inhibitor in COVID-19.

CONCLUSION

Effective ACE-II, GAK, AAK1, and protease 3C blockers can serve as a novel therapeutic approach to block the binding and attachment of the main COVID-19 protease (PDB ID: 6LU7) to the host cell and thus inhibit the infection at AT2 receptors in the lung. The novel compound nCorv- EMBS herein proposed stands as a promising inhibitor to be evaluated further for COVID-19 treatment.

Experimental and theoretical investigations on the interaction of glucose molecules with myoglobin in the aqueous solution using theoretical and experimental methods

Osmolytes are generally well-known for the stabilization of proteins. The stabilizing impact of glucose on the dynamics and structure of myoglobin was probed through molecular simulation' docking and spectroscopic procedures. Using thermal stability examinations, the thermodynamic folding properties, point of melting temp. (T_m), thermodynamic enthalpy change (ΔH°) and thermodynamic entropy change (ΔS°) were determined to find out the depiction of myoglobin folding. Glucose operated as an enhancer relative to myoglobin stabilization. The quenching static model was demonstrated by fluorescence spectroscopy. There was one binding site. According to the spectroscopy analysis, glucose was capable of protecting the native structural conformation of protein as well as preventing from protein unfolding. The fluorescence spectroscopy together with simulation through molecular docking method revealed that definitely hydrogen bonding plus van der Waals forces had major contributions to the stabilization of the myoglobin-glucose complex. Hence, the direct interactions contributed slightly to the stabilization impact whereas indirect interactions resulted from the hydration arise from a molecular mechanism primarily inducing the glucose stabilizing impacts. An elevation occurred in the T_m of the myoglobin-glucose complex because of the greater H-bond creation and limited surface hydrophobic activity. Our findings indicate that glucose was capable of protecting the native conformation of myoglobin, clearly describing that glucose stabilization is preferred to be omitted from myoglobin surface. This is because water is more inclined to provide desirable interacting with myoglobin functional groups as compared to glucose. Also, MD results confirmed that the structural changes of myoglobin is the effect of complex formation with glucose.Communicated by Ramaswamy H. Sarma.

Virtual Screening and In Silico Simulation Analysis for Rapid and Efficient Identification of Novel Natural GPR40 Agonist

Background:

Diabetes is the foremost health problem worldwide predisposing to increased mortality and morbidity. The available synthetic drugs have serious side effects and thus, emphasize further need to develop effective medication therapy. GPR40 represents an interesting target for developing novel antidiabetic drug. In the current study, searching of potential natural hit candidate as agonist by using structure based computational approach.

Methods:

The GPR40 agonistic activity of natural compounds was searched by using Maestro through docking and Molecular Dynamics (MD) simulation application. Virtual screening by using IBScreen library of natural compounds was done and the binding modes of newer natural entity(s) were investigated. Further, MD studies of the GPR40 complex with the most promising hit found in this study justified the stability of these complexes.

Results:

The silicone chip-based approach recognized the most capable six hits and the ADME prediction aided the exploration of their pharmacokinetic potential. In this study, the obtained hit (ZINC70692253) after the use of exhaustive screening having binding energy -107.501 kcal/mol and root mean square deviation of hGPR40-ZINC70692253 is around 3.5 Å in 20 ns of simulation.

Conclusion:

Successful application of structure-based computational screening gave a novel candidate from Natural Product library for diabetes treatment. So, Natural compounds may tend to cure diabetes with lesser extent of undesirable effects in comparison to synthetic compounds and these novel screened compounds may show a plausible biological response in the hit to lead finding of drug development process. To the best of our knowledge, this is the first example of the successful application of SBVS to discover novel natural hit compounds using hGPR40.

Rational drug repurposing for cancer by inclusion of the unbiased molecular dynamics simulation in the structure-based virtual screening approach: Challenges and breakthroughs

Managing cancer is now one of the biggest concerns of health organizations. Many strategies have been developed in drug discovery pipelines to help rectify this problem and two of the best ones are drug repurposing and computational methods. The combination of these approaches can have immense impact on the course of drug discovery. In silico drug repurposing can significantly reduce the time, the cost and the effort of drug development. Computational methods such as structure-based drug design (SBDD) and virtual screening can predict the potentials of small molecule binders, such as drugs, for having favorable effect on a particular molecular target. However, the demand for accuracy and efficiency of SBDD requires more sophisticated and complicated approaches such as unbiased molecular dynamics (UMD) simulation that has been recently introduced. As a complementary strategy, the knowledge acquired from UMD simulations can increase the chance of finding the right candidates and the pipeline of its administration is introduced and discussed in this review. An elaboration of this pipeline is also made by detailing an example, the binding and unbinding pathways of dasatinib-c-Src kinase complex, which shows that how influential this method can be in rational drug repurposing in cancer treatment.

Discovery of promising FtsZ inhibitors by E-pharmacophore, 3D-QSAR, molecular docking study, and molecular dynamics simulation

Asbtract Filamentous temperature-sensitive protein Z (FtsZ), playing a key role in bacterial cell division, is regarded as a promising target for the design of antimicrobial agent. This study is looking for potential high-efficiency FtsZ inhibitors. Ligand-based pharmacophore and E-pharmacophore, virtual screening and molecular docking were used to detect promising FtsZ inhibitors, and molecular dynamics simulation was used to study the stability of protein-ligand complexes in this paper. Sixty-three inhibitors from published literatures with pIC₅₀ ranging from 2.483 to 5.678 were collected to develop ligand-based pharmacophore model. 4DXD bound with 9PC was selected to develop the E-pharmacophore model. The pharmacophore models validated by test set method and decoy set were employed for virtual screening to exclude inactive compounds against ZINC database. After molecular docking, ADME analysis, IFD docking and MM-GBSA, 8 hits were identified as potent FtsZ inhibitors. A 50 ns molecular dynamics simulation was implemented on the compounds to assess the stability between potent inhibitors and FtsZ. The results indicated that the candidate compounds had a high docking score and were strongly combined with FtsZ by forming hydrogen bonding interactions with key amino acid residues, and van der Waals forces and hydrophobic interactions had significant contribution to the stability of the binding. Molecular dynamics simulation results showed that the protein-ligand compounds performed well in both the stability and flexibility of the simulation process.

Experimental and theoretical investigations on the interaction of l-methionine molecules with α-chymotrypsin in the aqueous solution using various methods

l-Methionine (l-Met) is one of the necessary amino acids that play unparalleled roles, influencing both the protein structure and metabolism. Understanding the interactions between proteins and small molecules can be realized by various perspectives, and this is significant for the progression of basic sciences and drug development. In this study, the variations in the stability, function, and structure of α-Chymotrypsin (α-Chy) in the presence of l-Met were investigated using spectroscopic and computational approaches. The results of the UV-vis absorption demonstrated that α-Chy had a maximum peak at 280 nm due to the Trp residue. Hyperchromism shift was seen in the presence of l-Met. Ground state system was formed in the presence of l-Met, as confirmed by the fluorescence studies. Fluorescence variations also revealed static quenching. The CD spectra also represented the alteration of the enzyme with an increase in the α-helix and a decrease in the β-sheet. The activity of α-Chy was incremented in the presence of l-Met. Therefore, l-Met served as an activator. Molecular docking results also indicated a negative amount for the Gibbs free energy of the binding of l-Met to the enzyme. α-Chy became more stable in the presence of l-Met, based on the molecular dynamics simulation.

Discover potential inhibitors for PFKFB3 using 3D-QSAR, virtual screening, molecular docking and molecular dynamics simulation

The 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase-3 (PFKFB3) is a master regulator of glycolysis in cancer cells by synthesizing fructose-2,6-bisphosphate (F-2,6-BP), a potent allosteric activator of phosphofructokinase-1 (PFK-1), which is a rate-limiting enzyme of glycolysis. PFKFB3 is an attractive target for cancer treatment. It is valuable to discover promising inhibitors by using 3D-QSAR pharmacophore modeling, virtual screening, molecular docking and molecular dynamics simulation. Twenty molecules with known activity were used to build 3D-QSAR pharmacophore models. The best pharmacophore model was ADHR called Hypo1, which had the highest correlation value of 0.98 and the lowest RMSD of 0.82. Then, the Hypo1 was validated by cost value method, test set method and decoy set validation method. Next, the Hypo1 combined with Lipinski's rule of five and ADMET properties were employed to screen databases including Asinex and Specs, total of 1,048,159 molecules. The hits retrieved from screening were docked into protein by different procedures including HTVS, SP and XP. Finally, nine molecules were picked out as potential PFKFB3 inhibitors. The stability of PFKFB3-lead complexes was verified by 40 ns molecular dynamics simulation. The binding free energy and the energy contribution of per residue to the binding energy were calculated by MM-PBSA based on molecular dynamics simulation.

Pharmacophore modeling, multiple docking, and molecular dynamics studies on Wee1 kinase inhibitors

Wee1-like protein kinase (Wee1) is a tyrosine kinase that regulates the G₂ checkpoint and prevents entry into mitosis in response to DNA damage. Based on a series of signaling pathways initiated by Wee1, Wee1 has been recognized as a potential target for cancer therapy. To discover potent Wee1 inhibitors with novel scaffolds, ligand-based pharmacophore model has been built based on 101 known Wee1 inhibitors. Then the best pharmacophore model, AADRRR.340, with good partial least square (PLS) statistics (R² = 0.9212, Q² = 0.7457), was selected and validated. The validated model was used as a three-dimensional (3D) search query for databases virtual screening. The filtered molecules were further analyzed and refined by Lipinski's rule of 5, multiple docking procedures (high throughput virtual screening (HTVS), standard precision (SP), genetic optimization for ligand docking (GOLD), extra precision (XP), and unique quantum polarized ligand docking (QPLD)); absorption, distribution, metabolism, excretion, and toxicity (ADMET) screening; and the Prime/molecular mechanics generalized born surface area (MM-GBSA) method binding free energy calculations. Eight leads were identified as potential Wee1 inhibitors, and a 50 ns molecular dynamics (MD) simulation was carried out for top four inhibitors to predict the stability of ligand-protein complex. Molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) based on MD simulation and the energy contribution per residue to the binding energy were calculated. In the end, three hits with good stabilization and affinity to protein were identified. Communicated by Ramaswamy H. Sarma.

Enzymatic activation of double-targeted 5'-O-L-valyl-decitabine prodrug by biphenyl hydrolase-like protein and its molecular design basis

A primary focus of this research was to explore the activation process and mechanism of decitabine (5-aza-2'-deoxycytidine, DAC) prodrug. Recently, it has been reported that biphenyl hydrolase-like protein (BPHL) can play an important role in the activation of some amino acid nucleoside prodrugs with a general preference for hydrophobic amino acids and 5'-esters. Therefore, we put forward a bold hypothesis that this novel enzyme may be primarily responsible for the activation process of DAC prodrug as well. 5'-O-L-valyl-decitabine (L-val-DAC) was synthesized before and can be transported across biological membranes by the oligopeptide transporter (PEPT1), granting it much greater utility in vivo. In this report, L-val-DAC was found to be a good substrate of BPHL protein (K _m 0.59 mM; k _cat/K _m 553.69 mM^-1 s^-1). After intestinal absorption, L-val-DAC was rapidly and almost completely hydrolyzed to DAC and L-valine. The catalysis was mainly mediated by the BPHL hydrolase and resulted in the intestinal first-pass effect of L-val-DAC after oral administration in Sprague-Dawley rats with cannulated jugular and portal veins. The structural insights using computational molecular docking showed that BPHL had a unique binding mode for L-val-DAC. As a fundamental basis, the simulation was employed to explain the catalytic mechanism in molecular level. In conclusion, BPHL was at least one of the primary candidate enzymes for L-val-DAC prodrug activation. This promising double-targeted prodrug approach have more advantages than the traditional targeted designs due to its higher transport and more predictable activation, thereby leading to a favorable property for oral delivery.

Cloning, Expression, and Characterization of a Novel L-Arabinose Isomerase from the Psychrotolerant Bacterium Pseudoalteromonas haloplanktis

L-Arabinose isomerase (L-AI, EC 5.3.1.4) catalyzes the isomerization between L-arabinose and L-ribulose, and most of the reported ones can also catalyze D-galactose to D-tagatose, except Bacillus subtilis L-AI. In this article, the L-AI from the psychrotolerant bacterium Pseudoalteromonas haloplanktis ATCC 14393 was characterized. The enzyme showed no substrate specificity toward D-galactose, which was similar to B. subtilis L-AI but distinguished from other reported L-AIs. The araA gene encoding the P. haloplanktis L-AI was cloned and overexpressed in E. coli BL21 (DE3). The recombinant enzyme was purified by one-step nickel affinity chromatography . The enzyme displayed the maximal activity at 40 °C and pH 8.0, and showed more than 75 % of maximal activity from pH 7.5-9.0. Metal ion Mn²⁺ was required as optimum metal cofactor for activity simulation, but it did not play a significant role in thermostability improvement as reported previously. The Michaelis-Menten constant (K _m), turnover number (k _cat), and catalytic efficiency (k _cat/K _m) for substrate L-arabinose were measured to be 111.68 mM, 773.30/min, and 6.92/mM/min, respectively. The molecular docking results showed that the active site residues of P. haloplanktis L-AI could only immobilize L-arabinose and recognized it as substrate for isomerization.

An Investigation on the Quantitative Structure-Activity Relationships of the Anti-Inflammatory Activity of Diterpenoid Alkaloids

Diterpenoid alkaloids are extracted from plants. These compounds have broad biological activities, including effects on the cardiovascular system, anti-inflammatory and analgesic actions, and anti-tumor activity. The anti-inflammatory activity was determined by carrageenan-induced rat paw edema and experimental trauma in rats. The number of studies focused on the determination, quantitation and pharmacological properties of these alkaloids has increased dramatically during the past few years. In this work we built a dataset composed of 15 diterpenoid alkaloid compounds with diverse structures, of which 11 compounds were included in the training set and the remaining compounds were included in the test set. The quantitative chemistry parameters of the 15 diterpenoid alkaloids compound were calculated using the HyperChem software, and the quantitative structure-activity relationship (QSAR) of these diterpenoid alkaloid compounds were assessed in an anti-inflammation model based on half maximal effective concentration (EC₅₀) measurements obtained from rat paw edema data. The QSAR prediction model is as follows: log ( E C 50 ) = - 0.0260 × SAA + 0.0086 × SAG + 0.0011 × VOL - 0.0641 × HE - 0.2628 × LogP - 0.5594 × REF - 0.2211 × POL - 0.1964 × MASS + 0.088 × BE + 0.1398 × HF (R² = 0.981, Q² = 0.92). The validated consensus EC₅₀ for the QSAR model, developed from the rat paw edema anti-inflammation model used in this study, indicate that this model was capable of effective prediction and can be used as a reliable computational predictor of diterpenoid alkaloid activity.

Comparison of the interaction between lactoferrin and isomeric drugs

The binding properties of pentacyclic triterpenoid isomeric drugs, i.e. ursolic acid (UA) and oleanolic acid (OA), to bovine lactoferrin (BLF) have been studied by molecule modeling, fluorescence spectroscopy, UV-visible absorbance spectroscopy and infrared spectroscopy (IR). Molecular docking, performed to reveal the possible binding mode or mechanism, suggested that hydrophobic interaction and hydrogen bonding play important roles to stabilize the complex. The results of spectroscopic measurements showed that the two isomeric drugs both strongly quenched the intrinsic fluorescence of BLF through a static quenching procedure although some differences between UA and OA binding strength and non-radiation energy transfer occurred within the molecules. The number of binding sites was 3.44 and 3.10 for UA and OA, respectively, and the efficiency of Förster energy transfer provided a distance of 0.77 and 1.21nm for UA and OA, respectively. The conformation transformation of BLF affected by the drugs conformed to the "all-or-none" pattern. In addition, the changes of the ratios of α-helices, β-sheets and β-turns of BLF during the process of the interaction were obtained. The results of the experiments in combination with the calculations showed that there are two modes of pentacyclic triterpenoid binding to BLF instead of one binding mode only governed by the principle of the lowest bonding energy.

Different Types of Molecular Docking Based on Variations of Interacting Molecules

Molecular docking plays an important role in drug discovery research by facilitating target identification, target validation, virtual screening for lead identification and lead optimization. Depending upon the nature of the disease of interest, targets can be either protein or DNA while drugs are mostly organic small molecules. Different types of molecular docking techniques like protein-protein or protein-DNA or protein-small molecule or DNA-small molecule are employed for achieving the above mentioned objectives. This chapter provides a clear idea of the position of molecular docking in drug discovery with detailed discussion on different types of molecular docking based on the varieties of interacting partners. Subsequently the authors provide a detailed list of tools that can be used for docking in drug discovery and discus some examples of molecular docking in drug discovery before concluding with a remark on future areas of improvement in molecular docking related to drug discovery.

Oligomeric Conjugated Polyelectrolytes Display Site-Preferential Binding to an MS2 Viral Capsid

Opportunistic bacteria and viruses are a worldwide health threat prompting the need to develop new targeting modalities. A class of novel synthetic poly(phenylene ethynylene) (PPE)-based oligomeric conjugated polyelectrolytes (OPEs) have demonstrated potent wide-spectrum biocidal activity. A subset of cationic OPEs display high antiviral activity against the MS2 bacteriophage. The oligomers have been found to inactivate the bacteriophage and perturb the morphology of the MS2 viral capsid. However, details of the initial binding and interactions between the OPEs and the viruses are not well understood. In this study, we use a multiscale computational approach, including random sampling, molecular dynamics, and electronic structure calculations, to gain an understanding of the molecular-level interactions of a series of OPEs that vary in length, charge, and functional groups with the MS2 capsid. Our results show that OPEs strongly bind to the MS2 capsid protein assembly with binding energies of up to -30 kcal/mol. Free-energy analysis shows that the binding is dominated by strong van der Waals interactions between the hydrophobic OPE backbone and the capsid surface and strong electrostatic free energy contributions between the OPE charged moieties and charged residues on the capsid surface. This knowledge provides molecular-level insight into how to tailor the OPEs to optimize viral capsid disruption and increase OPE efficacy to target amphiphilic protein coats of icosahedral-based viruses.

Production of anti-amoxicillin ScFv antibody and simulation studying its molecular recognition mechanism for penicillins

The molecular recognition mechanism of an antibody for its hapten is very interesting. The objective of this research was to study the intermolecular interactions of an anti-amoxicillin antibody with penicillin drugs. The single chain variable fragment (ScFv) antibody was generated from a hybridoma cell strain excreting the monoclonal antibody for amoxicillin. The recombinant ScFv antibody showed similar recognition ability for penicillins to its parental monoclonal antibody: simultaneous recognizing 11 penicillins with cross-reactivities of 18-107%. The three-dimensional structure of the ScFv antibody was simulated by using homology modeling, and its intermolecular interactions with 11 penicillins were studied by using molecular docking. Results showed that three CDRs are involved in antibody recognition; CDR L3 Arg 100, CDR H3 Tyr226, and CDR H3 Arg 228 were the key contact amino acid residues; hydrogen bonding was the main antibody-drug intermolecular force; and the core structure of penicillin drugs was the main antibody binding position. These results could explain the recognition mechanism of anti-amoxicillin antibody for amoxicillin and its analogs. This is the first study reporting the production of ScFv antibody for penicillins and stimulation studying its recognition mechanism.

The drug binding sites and transport mechanism of the RND pumps from Mycobacterium tuberculosis: Insights from molecular dynamics simulations

RND permease superfamily drug efflux pumps are involved in multidrug transport and are attractive to study them for therapeutic purpose. In previous work we have classified 14 members of MmpL proteins belong to RND superfamily from Mycobacterium tuberculosis (Mtb) within its families [Sandhu P. and Akhter Y., 2015. Int. J. Med. Microbiol., 305:413-423]. In this study, structures of these proteins are homology modelled. The drug binding sites and channels are identified using local micro-stereochemistry and charge densities. Potential transport mechanism based on differential structural behaviour in the absence and on the binding of drug molecules is explained using the molecular dynamics simulation results. Our studies show two potential drug binding sites positioned at opposite ends of the transport tunnel leading from cytoplasmic to the periplasmic space across MmpL5 trimer. The drug binding have effects on the structural conformation of the protein leading to molecular-scale peristaltic movements. The free binding energy calculations reveal that the subsequent binding events are interdependent and may have implications on transport mechanism. Two drug binding sites and a continuous channel in the RND pump have been reported. The proposed ligand binding mechanism shows peristaltic movements in the channel leading to the drug efflux. This study would be helpful in understanding the molecular basis of drugs resistance in Mtb.

Different Types of Molecular Docking Based on Variations of Interacting Molecules

Molecular docking plays an important role in drug discovery research by facilitating target identification, target validation, virtual screening for lead identification and lead optimization. Depending upon the nature of the disease of interest, targets can be either protein or DNA while drugs are mostly organic small molecules. Different types of molecular docking techniques like protein-protein or protein-DNA or protein-small molecule or DNA-small molecule are employed for achieving the above mentioned objectives. This chapter provides a clear idea of the position of molecular docking in drug discovery with detailed discussion on different types of molecular docking based on the varieties of interacting partners. Subsequently the authors provide a detailed list of tools that can be used for docking in drug discovery and discus some examples of molecular docking in drug discovery before concluding with a remark on future areas of improvement in molecular docking related to drug discovery.

Investigation of the interaction of batatasin derivatives with human serum albumin using voltammetric and spectroscopic methods

The binding properties of batatasin derivatives with HSA were estimated by voltammetric, spectroscopic, and molecular docking methods. There were non-electroactive complexes formed between them. And the α-helix structure in HSA was reduced.

Evaluating Andrographolide as a Potent Inhibitor of NS3-4A Protease and Its Drug-Resistant Mutants Using In Silico Approaches

Current combination therapy of PEG-INF and ribavirin against the Hepatitis C Virus (HCV) genotype-1 infections is ineffective in maintaining sustained viral response in 50% of the infection cases. New compounds in the form of protease inhibitors can complement the combination therapy. Asunaprevir is new to the drug regiment as the NS3-4A protease inhibitor, but it is susceptible to two mutations, namely, R155K and D168A in the protein. Thus, in our study, we sought to evaluate Andrographolide, a labdane-diterpenoid from the Andrographis paniculata plant as an effective compound for inhibiting the NS3-4A protease as well as its concomitant drug-resistant mutants by using molecular docking and dynamic simulations. Our study shows that Andrographolide has best docking scores of −15.0862, −15.2322, and −13.9072 compared to those of Asunaprevir −3.7159, −2.6431, and −5.4149 with wild-type R155K and D168A mutants, respectively. Also, as shown in the MD simulations, the compound was good in binding the target proteins and maintains strong bonds causing very less to negligible perturbation in the protein backbone structures. Our results validate the susceptibility of Asunaprevir to protein variants as seen from our docking studies and trajectory period analysis. Therefore, from our study, we hope to add one more option in the drug regiment to tackle drug resistance in HCV infections.