Top Docking Research Articles

SchNetPack Hyperparameter Optimization for a More Reliable Top Docking Scores Prediction.

Options to improve the extrapolation power of the neural network designed using the SchNetPack package with respect to top docking scores prediction are presented. It is shown that hyperparameter tuning of the atomistic model representation (in the schnetpack.representation) improves the prediction of the top scoring compounds, which have characteristically a low incidence in randomized data sets for training of machine learning models. The prediction robustness is evaluated according to the mean square error (MSE) and the entropy of the average loss landscape decrease. Admittedly, the improvement of the top scoring compounds' prediction accuracy comes with the penalty of worsening the overall prediction power. It is revealed that the most impactful hyperparameter is the cutoff (5 Å is reported as the optimal choice). Other parameters (e.g., number of radial basis functions, number of interaction layers of the neural network, feature vector size or its batch size) are found to not affect the prediction robustness of the top scoring compounds in any comparable way relative to the cutoff. The MSE of the best docking score prediction (below -13 kcal/mol) improves from ca. 3.5 to 0.9 kcal/mol, while the prediction of less potent compounds (-13 to -11 kcal/mol) shows a lesser improvement, i.e., a decrease of MSE from 1.6 to 1.3 kcal/mol. Additionally, oversampling and undersampling of the training set with respect to the top scoring compounds' abundance is presented. The results indicate that the cutoff choice performs better than over- or undersampling of the training set, with undersampling performing better than oversampling.

The Molecular Docking of MAX Fungal Effectors with Plant HMA Domain-Binding Proteins.

Fungal effector proteins are important in mediating disease infections in agriculturally important crops. These secreted small proteins are known to interact with their respective host receptor binding partners in the host, either inside the cells or in the apoplastic space, depending on the localisation of the effector proteins. Consequently, it is important to understand the interactions between fungal effector proteins and their target host receptor binding partners, particularly since this can be used for the selection of potential plant resistance or susceptibility-related proteins that can be applied to the breeding of new cultivars with disease resistance. In this study, molecular docking simulations were used to characterise protein-protein interactions between effector and plant receptors. Benchmarking was undertaken using available experimental structures of effector-host receptor complexes to optimise simulation parameters, which were then used to predict the structures and mediating interactions of effector proteins with host receptor binding partners that have not yet been characterised experimentally. Rigid docking was applied for both the so-called bound and unbound docking of MAX effectors with plant HMA domain protein partners. All bound complexes used for benchmarking were correctly predicted, with 84% being ranked as the top docking pose using the ZDOCK scoring function. In the case of unbound complexes, a minimum of 95% of known residues were predicted to be part of the interacting interface on the host receptor binding partner, and at least 87% of known residues were predicted to be part of the interacting interface on the effector protein. Hydrophobic interactions were found to dominate the formation of effector-plant protein complexes. An optimised set of docking parameters based on the use of ZDOCK and ZRANK scoring functions were established to enable the prediction of near-native docking poses involving different binding interfaces on plant HMA domain proteins. Whilst this study was limited by the availability of the experimentally determined complexed structures of effectors and host receptor binding partners, we demonstrated the potential of molecular docking simulations to predict the likely interactions between effectors and their respective host receptor binding partners. This computational approach may accelerate the process of the discovery of putative interacting plant partners of effector proteins and contribute to effector-assisted marker discovery, thereby supporting the breeding of disease-resistant crops.

V-003 Parastomal & ileal conduit hernia: Top docking robotic eTEP approach

Abstract Aim Parastomal hernias are frequent in patients with end colostomy. Recently, the robotic approach for the treatment of this pathology had spread abroad. On the other hand, patients with an ileal conduit urinary diversion after a radical cystoprostatectomy have a high risk of incisional hernia development. The combination of both parastomal hernia and ileal conduit hernia is rare. The aim of this video is to describe a top docking robotic eTEP approach combining a modified retromuscular Sugarbaker technique (Pauli) with a retromuscular keyhole for the parastomal and ileal conduit hernias respectively. Material and Methods The case is a 71 years old man patient with a BMI of 36 who presented a progressive 2-year discomfort bulging around the end colostomy that turned in an intermittent constipation. Additionally, a progressive bulging started the last 6 months on the ileal conduit. An abdominal CT scan was made showing a bilateral ostomal defect with small bowel parastomal content and fat tissue in the ileal conduit. Once the patient was assessed, a top docking robotic eTEP approach was planned with a Xi Da Vinci robotic platform. The patient was in a hyperextended supine position. Result The patient was discharged 3 days after the operation with a mild pain controlled by oral medication. Conclusion The Pauli technique and retromuscular keyhole for combined parastomal and ileal conduit hernia performed by a top docking robotic approach is feasible, but a long follow-up is needed to demonstrate its effectiveness.

Identification of BACE-1 Inhibitors against Alzheimer's Disease through E-Pharmacophore-Based Virtual Screening and Molecular Dynamics Simulation Studies: An Insilco Approach.

Alzheimer is a severe memory and cognitive impairment neurodegenerative disease that is the most common cause of dementia worldwide and characterized by the pathological accumulation of tau protein and amyloid-beta peptides. In this study, we have developed E-pharmacophore modeling to screen the eMolecules database with the help of a reported co-crystal structure bound with Beta-Site Amyloid Precursor Protein Cleaving Enzyme 1 (BACE-1). Flumemetamol, florbetaben, and florbetapir are currently approved drugs for use in the clinical diagnosis of Alzheimer's disease. Despite the benefits of commercially approved drugs, there is still a need for novel diagnostic agents with enhanced physicochemical and pharmacokinetic properties compared to those currently used in clinical practice and research. In the E-pharmacophore modeling results, it is revealed that two aromatic rings (R19, R20), one donor (D12), and one acceptor (A8) are obtained, and also that similar pharmacophoric features of compounds are identified from pharmacophore-based virtual screening. The identified screened hits were filtered for further analyses using structure-based virtual screening and MM/GBSA. From the analyses, top hits such as ZINC39592220 and en1003sfl.46293 are selected based on their top docking scores (-8.182 and -7.184 Kcal/mol, respectively) and binding free energy (-58.803 and -56.951 Kcal/mol, respectively). Furthermore, a molecular dynamics simulation and MMPBSA study were performed, which revealed admirable stability and good binding free energy throughout the simulation period. Moreover, Qikprop results revealed that the selected, screened hits have good drug-likeness and pharmacokinetic properties. The screened hits ZINC39592220 and en1003sfl.46293 could be used to develop drug molecules against Alzheimer's disease.

Vitamin derivatives as potential drugs for Influenza Hemagglutinin

The objective of the study was to identify potential inhibitors of Influenza surface Hemagglutinin (HA), which plays key role in the entry and replication of Influenza virus into the host cell. As ligands, seven vitamins and their derivatives were selected after initial screening based on their metabolizable capacity with no reported side effects, for in silico studies. Docking, and Post docking analysis (X Score and Ligplot+) were performed against nine Influenza HA targets for the vitamins and its derivatives. ‘Vitamin Derivatives’ with top docking score were further analysed by MD Simulations and free energy was calculated using MMGBSA module. FMNNa and FMNCa displayed high binding free energy with Influenza HA, thereby exhibiting potential as HA inhibitors. Communicated by Ramaswamy H. Sarma

Carbapenem resistance gene crisis in A. baumannii: a computational analysis

Acinetobacter baumannii (A. baumannii) is one of the members of ESKAPE bacteria which is considered multidrug resistant globally. The objective of this study is to determine the protein docking of different antibiotic resistance gene (ARGs) in A. baumannii. In silico analysis of antibiotic resistance genes against carbapenem are the blaOXA-51, blaOXA-23, blaOXA-58, blaOXA-24, blaOXA-143, NMD-1 and IMP-1 in A. baumannii. The doripenem, imipenem and meropenem were docked to blaOXA-51 and blaOXA-23 using PyRx. The top docking energy was -5.5 kcal/mol by imipenem and doripenem and meropenem showed a binding score of -5. 2 kcal/mol each and blaOXA-23 energy was -4.3 kcal/mol by imipenem and meropenem showed a binding score of -2.3 kcal/mol, while doripenem showed the binding score of -3.4 kcal/mol. Similarly, doripenem imipenem and meropenem were docked to blaOXA-58, IMP-1, Rec A and blaOXA-143, with docking energy was -8.8 kcal/mol by doripenem and meropenem each while imipenem showed a binding score of -4.2 kcal/mol and with IMP-1 demonstrated their binding energies. was -5.7 kcal/mol by meropenem and doripenem showed a binding score of -5.3 kcal/mol, while imipenem showed a binding score of -4.5 kcal/mol. And docking energy was -4.9 kcal/mol by imipenem and meropenem showed binding energy of -3.6 kcal/mol each while doripenem showed a binding score of -3.9 kcal/mol in RecA and with blaOXA-143 docking energy was -3.0 kcal/mol by imipenem and meropenem showed a binding score of -1.9 kcal/mol, while doripenem showed the binding score of -2.5 kcal/mol respectively. Doripenem, imipenem, and meropenem docking findings with blaOXA-24 confirmed their binding energies. Doripenem had the highest docking energy of -5.5 kcal/mol, meropenem had a binding score of -4.0 kcal/mol, and imipenem had a binding score of -3.9 kcal/mol. PyRx was used to dock the doripenem, imipenem, and meropenem to NMD-1. Docking energies for doripenem were all – 4.0 kcal/mol, whereas meropenem had docking energy of -3.3 kcal/mol and imipenem was -1.50 kcal/mol. To the best of our knowledge the underlying mechanism of phenotypic with genotypic resistance molecular docking regarding carbapenem resistance A. baumannii is unclear. Our molecular docking finds the possible protein targeting mechanism for carbapenem-resistant A.baumannii.

In-silico screening and identification of potential bioactive compounds of Trifolium repens against pathogenic bacterial target proteins

From medicinal plants, a number of bioactive substances have been discovered and reported in the past, including flavonoids, alkaloids, phenols, tannins, saponins, and sterols. To identify the most effective natural bioactive compounds against the target proteins of bacteria, the bioactive compounds from T. repens have been studied in the current work. Numerous infections like chronic sinusitis, otitis media, intravascular catheter infection, pacemaker infection, peritoneal dialysis, urinary infection, prosthetic joint infection, pulmonary infection, and keratitis are caused by biofilm-forming bacteria. The increased rate of biofilm formation has threatened life forms as they show resistance to most antibiotics. During in-silico analysis, among the nine bioactive compounds listed, two bioactive compounds namely Trifolin and Naringenin were selected as their biological activity has not been reported. The optimum binding affinities between ligand–protein were chosen after screening study of 22 target proteins from Staphylococcus aureus, Pseudomonas aeruginosa, Acinetobacter baumannii, Enterococcus faecalis, and Klebsiella pneumoniae. The effects of trifolin and naringenin, two naturally occurring bioactive chemicals from T. repens, on several bacterial biofilm target proteins were investigated. An in-silico study was carried out to determine the interaction between the ligand and receptor (target proteins). The top docking poses showed multiple conventional hydrogen bond, which indicated a high binding affinity with the bacterial target biofilm proteins.

Virtual Screening for SARS-CoV-2 Main Protease Inhibitory Peptides from the Putative Hydrolyzed Peptidome of Rice Bran.

The Coronavirus Disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to the loss of life and has affected the life quality, economy, and lifestyle. The SARS-CoV-2 main protease (Mpro), which hydrolyzes the polyprotein, is an interesting antiviral target to inhibit the spreading mechanism of COVID-19. Through predictive digestion, the peptidomes of the four major proteins in rice bran, albumin, glutelin, globulin, and prolamin, with three protease enzymes (pepsin, trypsin, and chymotrypsin), the putative hydrolyzed peptidome was established and used as the input dataset. Then, the prediction of the antiviral peptides (AVPs) was performed by online bioinformatics tools, i.e., AVPpred, Meta-iAVP, AMPfun, and ENNAVIA programs. The amino acid composition and cytotoxicity of candidate AVPs were analyzed by COPid and ToxinPred, respectively. The ten top-ranked antiviral peptides were selected and docked to the SARS-CoV-2 main protease using GalaxyPepDock. Only the top docking scored candidate (AVP4) was further analyzed by molecular dynamics simulation for one nanosecond. According to the bioinformatic analysis results, the candidate SARS-CoV-2 main protease inhibitory peptides were 7–33 amino acid residues and formed hydrogen bonds at Thr22–24, Glu154, and Thr178 in domain 2 with short bonding distances. In addition, these top-ten candidate bioactive peptides contain hydrophilic amino acid residues and have a positive net charge. We hope that this study will provide a potential starting point for peptide-based therapeutic agents against COVID-19.

A Computational Study of Carbazole Alkaloids from Murraya koenigii as Potential SARS-CoV-2 Main Protease Inhibitors.

Despite COVID-19 vaccination, immune escape of new SARS-CoV-2 variants has created an urgent priority to identify additional antiviral drugs. Targeting main protease (Mpro) expressed by SARS-CoV-2 is a therapeutic strategy for drug development due to its prominent role in viral replication cycle. Leaves of Murraya koenigii are used in various traditional medicinal applications and this plant is known as a rich source of carbazole alkaloids. Thus, this computational study was designed to investigate the inhibitory potential of carbazole alkaloids from Murraya koenigii against Mpro. Molecular docking was initially used to determine the binding affinity and molecular interactions of carbazole alkaloids and the reference inhibitor (3WL) in the active site of SARS-CoV-2 Mpro (PDB ID: 6M2N).The top scoring compounds were further assessed for protein structure flexibility, physicochemical properties and drug-likeness, pharmacokinetic and toxicity (ADME/T) properties, antiviral activity, and pharmacophore modeling. Five carbazole alkaloids (koenigicine, mukonicine, o-methylmurrayamine A, koenine, and girinimbine) displayed a unique binding mechanism that shielded the catalytic dyad of Mpro with stronger binding affinities and molecular interactions than 3WL. Furthermore, the compounds with high affinity displayed favorable physicochemical and ADME/T properties that satisfied the criteria for oral bioavailability and druggability. The pharmacophore modeling study shows shared pharmacophoric features of those compounds for their biological interaction with Mpro. During the molecular dynamics simulation, the top docking complexes demonstrated precise stability except koenigicine. Therefore, mukonicine, o-methylmurrayamine A, koenine, and girinimbine may have the potential to restrict SARS-CoV-2 replication by inactivating the Mpro catalytic activity.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12010-022-04138-6.

Drug repurposing against SARS-CoV-2 receptor binding domain using ensemble-based virtual screening and molecular dynamics simulations

Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has caused worldwide pandemic and is responsible for millions of worldwide deaths due to -a respiratory disease known as COVID-19. In the search for a cure of COVID-19, drug repurposing is a fast and cost-effective approach to identify anti-COVID-19 drugs from existing drugs. The receptor binding domain (RBD) of the SARS-CoV-2 spike protein has been a main target for drug designs to block spike protein binding to ACE2 proteins. In this study, we probed the conformational plasticity of the RBD using long molecular dynamics (MD) simulations, from which, representative conformations were identified using clustering analysis. Three simulated conformations and the original crystal structure were used to screen FDA approved drugs (2466 drugs) against the predicted binding site at the ACE2-RBD interface, leading to 18 drugs with top docking scores. Notably, 16 out of the 18 drugs were obtained from the simulated conformations, while the crystal structure suggests poor binding. The binding stability of the 18 drugs were further investigated using MD simulations. Encouragingly, 6 drugs exhibited stable binding with RBD at the ACE2-RBD interface and 3 of them (gonadorelin, fondaparinux and atorvastatin) showed significantly enhanced binding after the MD simulations. Our study shows that flexibility modeling of SARS-CoV-2 RBD using MD simulation is of great help in identifying novel agents which might block the interaction between human ACE2 and the SARS-CoV-2 RBD for inhibiting the virus infection.

Structural based screening of potential inhibitors of SMAD4: a step towards personalized medicine for gall bladder and other associated cancers

Gall bladder cancer (GBC) is an aggressive and most common malignancy of biliary tract lacking effective treatment due to unavailability of suitable biomarkers and therapeutics. SMAD4 is an essential mediator of transforming growth factor-β pathway involved in various cellular processes like growth, differentiation and apoptosis and also recognized as therapeutic target for GBC and other gastrointestinal tract cancers. In the present study, 3D structure of SMAD4 mutants was optimized through molecular dynamics simulation (MDS) along with wildtype. Furthermore, binding site of protein was predicted through hybrid approach and structural based virtual screening against two drug libraries was performed followed by docking. MDS of top docking score protein-ligand complexes were carried, and binding free energy was rescored. Two potential inhibitors, namely ZINC2098840 and ZINC8789167, were screened that displayed higher binding affinity towards mutant proteins compared with wildtype and both hydrophilic as well as hydrophobic interactions play a crucial role during protein-ligand binding. Current study identified novel and potent inhibitors of SMAD4 mutant that could be used as a drug candidate for the development of personalized medicine for gall bladder and other associated cancers.

In silico identification of angiotensin-1 converting enzyme inhibitors using text mining and virtual screening

Cardiovascular diseases are the world’s leading cause of death. Hypertension is an important risk factor for cardiovascular and renal diseases. Angiotensin-converting enzyme (ACE) can be a possible therapeutic target for managing angiotensin I conversion to angiotensin II and ultimately controlling hypertension. Indole is an significant fragment used in many medicines approved by FDA. For this reason, the molecules in their fragments containing” indol” keywords were taken from the Specs-SC (small compound) database. The predicted therapeutc activity values (TAV) of these compounds against hypertension were evaluated using binary models of QSAR by MetaCore/MetaDrug. For the 26 separate QSAR models of toxicity, molecules with measured TAV greater than 0.5 were used. 3792 non-toxic compounds were investigated by molecular docking study and molecular dynamics simulations for their ACE inhibitory activity. Glide standard precision (SP) of Maestro Molecular Modeling pocket was used to perform molecular docking. Short molecular dynamics (MD) simulations (5-ns) were carried out by initiating the top docking poses of selected 40 molecules. To quantitatively evaluate the predicted binding affinity of a screened compound, average MM/GBSA scores of screened ligands were calculated and based on their binding free energy values, hit compounds were identified for the long (100-ns) MD simulations. Root mean square deviation and root mean square fluctuations were also calculated to assess the structural characteristics and observe fluctuations of the 100-ns time scale. Thus, with the application of text mining and integrated molecular modeling we reported novel indole-based hit inhibitors for ACE-1. Communicated by Ramaswamy H. Sarma

Drug Re-positioning Studies for Novel HIV-1 Inhibitors Using Binary QSAR Models and Multi-target-driven In Silico Studies.

Current antiretroviral therapies against HIV involve the usage of at least two drugs that target different stages of HIV life cycle. However, potential drug interactions and side effects pose a problem. A promising concept for complex disease treatment is 'one molecule-multiple target' approach to overcome undesired effects of multiple drugs. Additionally, it is beneficial to consider drug re-purposing due to the cost of taking a drug into the market. Taking these into account, here potential anti-HIV compounds are suggested by virtually screening small approved drug molecules and clinical candidates. Initially, binary QSAR models are used to predict the therapeutic activity of around 7900 compounds against HIV and to predict the toxicity of molecules with high therapeutic activities. Selected compounds are considered for molecular docking studies against two targets, HIV-1 protease enzyme, and chemokine co-receptor CCR5. The top docking poses for all 549 molecules are then subjected to short (1 ns) individual molecular dynamics (MD) simulations and they are ranked based on their calculated relative binding free energies. Finally, 25 molecules are selected for long (200 ns) MD simulations, and 5 molecules are suggested as promising multi-target HIV agents. The results of this study may open new avenues for the designing of new dual HIV-1 inhibitor scaffolds.

Screening of phytochemical compounds of Tinospora cordifolia for their inhibitory activity on SARS-CoV-2: an in silico study

In the present study, we explored phytochemical constituents of Tinospora cordifolia in terms of its binding affinity targeting the active site pocket of the main protease (3CL pro) of SARS-CoV-2 using molecular docking study and assessed the stability of top docking complex of tinosponone and 3CL pro using molecular dynamics simulations with GROMACS 2020.2 version. Out of 11 curated screened compounds, we found the significant docking score for tinosponone, xanosporic acid, cardiofolioside B, tembetarine and berberine in Tinospora cordifolia. Based on the findings of the docking study, it was confirmed that tinosponone is the potent inhibitor of main protease of SARS-CoV-2 with the best binding affinity of −7.7 kcal/mol. Further, ADME along with toxicity analysis was studied to predict the pharmacokinetics and drug-likeness properties of five top hits compounds. The molecular dynamics simulation analysis confirmed the stability of tinosponone and 3CL pro complex with a random mean square deviation (RMSD) value of 0.1 nm. The computer-aided drug design approach proved that the compound tinosponone from T. cordifolia is a potent inhibitor of 3CL main protease of SARS-CoV-2. Further, the in vitro and in vivo-based testing will be required to confirm its inhibitory effect on SARS-CoV-2. Communicated by Ramaswamy H. Sarma

Investigation of novel indole-based HIV-1 protease inhibitors using virtual screening and text mining

Human immunodeficiency virus type 1 protease (HIV-1 PR) inhibitors have been used as possible therapeutic agents for HIV-1 infection in clinical study. Most of the HIV therapy-related problems usually stem from long-term opioid usage. The rapid development of drug-resistant variants limits the long-term effectiveness of current inhibitors as therapeutic agents. In addition, different side effects were reported. Further drug development is required to design new compounds which have similar efficacy as the drugs currently used in HIV infection but without having undesirable side effects. Indole derivatives were considered as one of the effective HIV inhibitors. Indole is an important fragment used in many FDAapproved medicines and used in various diseases. For this purpose, in this study the molecules containing” indole” keywords in their fragments are taken from the Specs-SC database which includes 212520 small molecules. 5194 molecules that include indole keywords are selected. These selected molecules are then screened against HIV-1 PR target protein using molecular docking simulations. Then the molecules are ranked according to the their docking scores. Top docking poses of ten ligands and FDA approved drug Amprenavir are subjected to 100 ns Molecular Dynamics (MD) simulations. Thus, by using combination of text mining and integrated molecular modeling approaches, we identified novel indole-based hits against HIV-1 PR. Communicated by Ramaswamy H. Sarma

Fast Identification of Possible Drug Treatment of Coronavirus Disease-19 (COVID-19) through Computational Drug Repurposing Study.

The recent outbreak of novel coronavirus disease-19 (COVID-19) calls for and welcomes possible treatment strategies using drugs on the market. It is very efficient to apply computer-aided drug design techniques to quickly identify promising drug repurposing candidates, especially after the detailed 3D structures of key viral proteins are resolved. The virus causing COVID-19 is SARS-CoV-2. Taking advantage of a recently released crystal structure of SARS-CoV-2 main protease in complex with a covalently bonded inhibitor, N3 (Liu et al., 10.2210/pdb6LU7/pdb), I conducted virtual docking screening of approved drugs and drug candidates in clinical trials. For the top docking hits, I then performed molecular dynamics simulations followed by binding free energy calculations using an end point method called MM-PBSA-WSAS (molecular mechanics/Poisson–Boltzmann surface area/weighted solvent-accessible surface area; Wang, Chem. Rev.2019, 119, 947831244000; Wang, Curr. Comput.-Aided Drug Des.2006, 2, 287; Wang; HouJ. Chem. Inf. Model., 2012, 52, 119922497310). Several promising known drugs stand out as potential inhibitors of SARS-CoV-2 main protease, including carfilzomib, eravacycline, valrubicin, lopinavir, and elbasvir. Carfilzomib, an approved anticancer drug acting as a proteasome inhibitor, has the best MM-PBSA-WSAS binding free energy, −13.8 kcal/mol. The second-best repurposing drug candidate, eravacycline, is synthetic halogenated tetracycline class antibiotic. Streptomycin, another antibiotic and a charged molecule, also demonstrates some inhibitory effect, even though the predicted binding free energy of the charged form (−3.8 kcal/mol) is not nearly as low as that of the neutral form (−7.9 kcal/mol). One bioactive, PubChem 23727975, has a binding free energy of −12.9 kcal/mol. Detailed receptor–ligand interactions were analyzed and hot spots for the receptor–ligand binding were identified. I found that one hot spot residue, His41, is a conserved residue across many viruses including SARS-CoV, SARS-CoV-2, MERS-CoV, and hepatitis C virus (HCV). The findings of this study can facilitate rational drug design targeting the SARS-CoV-2 main protease.

Structural and molecular bases of angiotensin-converting enzyme inhibition by bovine casein-derived peptides: an in silico molecular dynamics approach

The angiotensin-converting enzyme (ACE) plays a key role in blood pressure regulation process, and its inhibition is one of the main drug targets for the treatment of hypertension. Though various peptides from milk proteins are well-known for their ACE-inhibitory capacity, research devoted to understand the molecular bases of such property remain scarce, specifically for such peptides. Therefore, in this work, computational molecular docking and molecular dynamics calculations were performed to enlighten the intermolecular interactions involved in ACE inhibition by six different casein-derived peptides (FFVAPFPEVFGK, FALPQYLK, ALNEINQFYQK, YLGYLEQLLR, HQGLPQEVLNENLLR and NAVPITPTLNR). Two top ranked docking poses for each peptide (one with N- and the other C-terminal peptide extremity oriented towards the ACE active site) were selected for dynamic simulations (50 ns; GROMOS53A6 force field), and the results were correlated to in vitro ACE inhibition capacity. Two molecular features appeared to be essential for peptides to present high ACE inhibition capacity in vitro: i) to interact with the S1 active site residues (Ala354, Glu384, and Tyr523) by hydrogen bonds; ii) to interact with Zn2+ coordinated residues (His383, His387, and Glu411) by short-lenght hydrogen bonds, as observed in the cases of ALNEINQFYQK (IACE = 80.7%), NAVPITPTLNR (IACE = 80.7%), and FALPQYLK (IACE = 79.0%). Regardless of the temporal stability of these strong interactions, they promoted some disruption of Zn2+ tetrahedral coordination during the molecular dynamics trajectories, and were pointed as the main reason for the greatest ACE inhibition by these peptides. On the other hand, peptides with intermediate inhibition capacity (50% < IACE < 45%) interacted mainly by weaker interactions (e.g.: electrostatic and hydrophobic) with the Zn2+ coordinated residues, and were not able to change significantly its tetrahedral coordination structure. These findings may: i) assist the discrimination in silico of “good” and “bad” ACE-inhibitory peptides from other food sources, and/or ii) aid in designing de novo new molecules with ACE-inhibitory capacity. Communicated by Ramaswamy Sarma

Design of Potential IKK-β Inhibitors using Molecular Docking and Molecular Dynamics Techniques for their Anti-cancer Potential.

To evaluate a set of seventy phytochemicals for their potential ability to bind the inhibitor of nuclear factor kappaB kinase beta (IKK-β) which is a prime target for cancer and inflammatory diseases. Seventy phytochemicals were screened against IKK-β enzyme using DFT-based molecular docking technique and the top docking hits were carried forward for molecular dynamics (MD) simulation protocols. The ADME-Toxicity analysis was also carried out for the top docking hits. Sesamin, matairesinol and resveratrol were found to be the top docking hits with a total score of -413 kJ/mol, -398.11 kJ/mol and 266.73 kJ/mol, respectively. Glu100 and Gly102 were found to be the most common interacting residues. The result from MD simulation observed a stable trajectory with a binding free energy of -107.62 kJ/mol for matairesinol, -120.37 kJ/mol for sesamin and -40.56 kJ/mol for resveratrol. The ADME-Toxicity prediction observed that these compounds fall within the permissible area of Boiled-Egg and it does not violate any rule for pharmacological criteria, drug-likeness etc. Conclusion: The study interprets that dietary phytochemicals are potent inhibitors of IKK-β enzyme with favorable binding affinity and less toxic effects. In fact, there is a gradual rise in the use of plant-derived molecules because of its lesser side effects compared to chemotherapy. The study has also provided an insight by which the phytochemicals inhibited the IKK-β enzyme. The investigation would also provide in understanding the inhibitory mode of certain dietary phytochemicals in treating cancer.

Therapeutic potential of IKK-β inhibitors from natural phenolics for inflammation in cardiovascular diseases.

Cardiovascular disease (CVDs) is a chronic disease with the highest morbidity and mortality in the world. Previous studies have suggested that preventing inflammation serves an efficient role in protection against cardiovascular diseases. Modulation of IKK-β activity can be used to treat and control CVDs associated with chronic inflammation, which targets the phosphorylation of IκB following the release of the RelA complex, and then translocates to the nucleus, eventually triggering the transcription of several genes that induce chemokines, cytokines, and adhesion molecules. Most importantly, the IκB kinase (IKK) complex is involved in transcriptional activation by phosphorylating the inhibitory molecule IkBα, enabling activation of NF-κB. Phenolic compounds possess cardioprotective potential that may be related to modulating inflammatory responses involved in CVDs. The SystemsDock analysis was used to explore whether 38 active compounds inhibit IKK-β activity based on literature. Docking results showed that the top docking score of three chemical compounds were icariin, salvianolic acid B, and plantainoside D in all compounds. Icariin, salvianolic acid B, and plantainoside D are the most promising IKKβ inhibitors. These phytochemicals could be helpful to find the lead compounds on designing and developing novel cardioprotective agents.

Enhanced sampling of protein conformational states for dynamic cross-docking within the protein-protein docking server SwarmDock.

The formation of specific protein‐protein interactions is often a key to a protein's function. During complex formation, each protein component will undergo a change in the conformational state, for some these changes are relatively small and reside primarily at the sidechain level; however, others may display notable backbone adjustments. One of the classic problems in the protein‐docking field is to be able to a priori predict the extent of such conformational changes. In this work, we investigated three protocols to find the most suitable input structure conformations for cross‐docking, including a robust sampling approach in normal mode space. Counterintuitively, knowledge of the theoretically best combination of normal modes for unbound‐bound transitions does not always lead to the best results. We used a novel spatial partitioning library, Aether Engine (see Supplementary Materials), to efficiently search the conformational states of 56 receptor/ligand pairs, including a recent CAPRI target, in a systematic manner and selected diverse conformations as input to our automated docking server, SwarmDock, a server that allows moderate conformational adjustments during the docking process. In essence, here we present a dynamic cross‐docking protocol, which when benchmarked against the simpler approach of just docking the unbound components shows a 10% uplift in the quality of the top docking pose.