Improvement of binding pose prediction of the MR1 covalent ligands by inclusion of simple pharmacophore constraints and structural waters in the docking process.

The major histocompatibility complex (MHC) class I-related molecule, MR1, is a key component of the immune system, presenting antigens to T-cell receptors (TCRs) and modulating the immune response against various antigens. MR1 possesses a compact ligand-binding pocket despite its ability to interact with ligands that can have either agonistic or antagonistic effects on the immune system. Agonistic ligands can stimulate the immune response, while antagonistic ligands do not elicit an immune response. In most cases, ligand binding to MR1 is mediated through a covalent bond with Lys43. However, recent studies have suggested that a variety of small molecules can interact with the MR1-binding site. In this study, we have used several approaches to improve the binding pose prediction of covalent ligands to MR1, including docking in mutated receptors, and imposing simple pharmacophore constraints and structural water molecules. The careful assignment of pharmacophore constraints and inclusion of structural water molecules in the challenging docking process of covalent docking improved the binding pose prediction and virtual screening performance. In a retrospective virtual screening, the proposed approach exhibited EF1% and EF2% values of 7.4 and 5.5, respectively. Conversely, when using the mutated receptor, both EF1% and EF2% were recorded as 0 for the conventional docking method. The performance of the pharmacophore constraints was also evaluated on other covalent docking cases, and compared to previously reported results for common covalent docking methods. The proposed approach achieved an average RMSD of 2.55, while AutoDock4, CovDock, FITTED, GOLD, ICM-Pro, and MOE exhibited average RMSD values of 3.0, 2.93, 3.04, 4.93, 2.44, and 3.36, respectively. Our results demonstrate that the inclusion of simple pharmacophore constraints and structural waters can improve the prediction of binding poses of covalent ligands to MR1, which can aid in the discovery of novel immunotherapeutic agents. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-023-03694-w.

Evaluation of serum nitric oxide synthase levels in patients with coronary slow flow based on corrected TIMI frame count.

Introduction: The coronary slow flow phenomenon (CSFP) finding in angiography is characterized by the delayed filling of the terminal vessels without significant epicardial coronary disease. The endothelium performs a vital role in cardiovascular homeostasis by releasing vasoactive substances. Endothelial cells produce nitric oxide (NO) as one of these essential compounds. Three isoforms of nitric oxide synthase (NOS) are endothelial nitric oxide synthase (eNOS), neuronal nitric oxide synthase (nNOS), and induced nitric oxide synthase (iNOS). We aimed to determine the role of NOS in the development of CSFP as the first human study. Material and methods: A total of 129 patients who met the inclusion criteria were enrolled in the study. The patients were classified into five groups based on the results of coronary angiography: Group 1 without coronary artery disease (CAD) and without CSF, group 2 without CAD and with CSF, group 3 with CAD (< 50%) and without CSF, group 4 with CAD (50-90%) and without CSF, and group 5 with CAD and CSF. The serum level of NOS was determined in the participants. Coronary flow was quantified in patients with CSFP using the corrected TIMI frame count (CTFC) method, and the correlation between the levels of this biomarker and CTFC was investigated. Results: In this study, the NOS serum levels were not significantly correlated with the mean CTFC. Since the total amount of NOS was measured as a result of 3 isoforms of this enzyme, the lack of correlation could be related to increased iNOS level and decreased eNOS concentration. Conclusions: These results should be confirmed by more human studies.

Docetaxel encapsulation in nanoscale assembly micelles of folate-PEG-docetaxel conjugates for targeted fighting against metastatic breast cancer in vitro and in vivo.

Due to the high frequency and mortality of breast cancer, developing efficient targeted drug delivery systems for frightening against this malignancy is among cancer research priorities. The aim of this study was to synthesize a targeted micellar formulation of docetaxel (DTX) using DTX, folic acid (FA) and polyethylene glycol (PEG) conjugates as building blocks. In the current study, two therapeutic polymers consisting of FA-PEG-DTX and PEG-DTX conjugates were synthesized and implemented to form folate-targeted and non-targeted micelles. Dissipative particle dynamics (DPD) method was used to simulate the behavior of the nanoparticle. The anti-cancer drug, DTX was loaded in to the micelles via solvent switching method in order to increase its solubility and stability. The cytotoxicity of the targeted and non-targeted formulations was evaluated against 4T1 and CHO cell lines. In vivo therapeutic efficiency was studied using ectopic tumor model of metastatic breast cancer, 4T1, in Female BALB/c mice. The successful synthesis of therapeutic polymers, FA-PEG-DTX and PEG-DTX were confirmed implementing 1HNMR spectral analysis. The size of DTX-loaded non-targeted and targeted micelles were 176.3 ± 8.3 and 181 ± 10.1 nm with PDI of 0.23 and 0.17, respectively. Loading efficiencies of DTX in non-targeted and targeted micelles were obtained to be 85% and 82%, respectively. In vitro release study at pH = 7.4 and pH = 5.4 showed a controlled and continuous drug release for both formulations, that was faster at pH = 5.4 (100% drug release within 120 h) than at pH = 7.4 (80% drug release within 150 h). The targeted formulation showed a significant higher cytotoxicity against 4T1 breast cancer cells (high expression of folate receptor) within the range of 12.5 to 200 µg/mL in comparison with no-targeted one. However, there was no significant difference between the cytotoxicity of the targeted and non-targeted formulations against CHO cell line as low-expressed cell line. In accordance with in vitro investigation, in vivo studies verified the ideal anti-tumor efficacy of the targeted formulation compared to Taxotere and non-targeted formulation. Based on the obtained data, FA-targeted DTX-loaded nano-micelles significantly increased the therapeutic efficacy of DTX and therefore can be considered as a new potent platform for breast cancer chemotherapy.

Evaluation of the performance of various machine learning methods on the discrimination of the active compounds.

Machine learning (ML) method performances, including deep learning (DL) on a diverse set with or without feature selection (FS), were evaluated. The superior performance of DL on small sets has not been approved previously. On the other hand, the available sets for the newly identified targets usually are limited in terms of size. It was explored whether the FS, hyperparameters search, and using ensemble model are able to improve the ML and DL performance on the small sets. The QSAR classifier models were developed using K-nearest (KN) neighbors, DL, random forest (RF), naïve Bayesian (NB) classification, support vector machine (SVM), and logistic regression (LR). Generally, the best individual performers were DL and SVM. The LR had a similar performance to the DL and SVM on the small subsets. The nested cross-validation method was able to include different feature vectors in combination with different ML methods to generate an ensemble model for the datasets with similar performance to the best performers. The general performance for the baseline NB model was Matthews correlation coefficient = 0.356, and it was improved to around 0.66 and 0.63 by NB assisted FS with subsequent SVM/DL classification and an ensemble model, respectively.

Bioinformatics analysis of the genes involved in the extension of prostate cancer to adjacent lymph nodes by supervised and unsupervised machine learning methods: The role of SPAG1 and PLEKHF2.

The present study aimed to identify the genes associated with the involvement of adjunct lymph nodes of patients with prostate cancer (PCa) and to provide valuable information for the identification of potential diagnostic biomarkers and pathological genes in PCa metastasis. The most important candidate genes were identified through several machine learning approaches including K-means clustering, neural network, Naïve Bayesian classifications and PCA with or without downsampling. In total, 21 genes associated with lymph nodes involvement were identified. Among them, nine genes have been identified in metastatic prostate cancer, six have been found in the other metastatic cancers and four in other local cancers. The amplification of the candidate genes was evaluated in the other PCa datasets. Besides, we identified a validated set of genes involved in the PCa metastasis. The amplification of SPAG1 and PLEKHF2 genes were associated with decreased survival in patients with PCa.

Corrigendum to "Evaluation of the Correlation between Serum Concentrations of Asymmetric Dimethylarginine and Corrected TIMI Frame Count in Patients with Slow Coronary Flow".

[This corrects the article DOI: 10.1155/2020/4592190.].

A Random Forest Model to Predict the Activity of a Large Set of Soluble Epoxide Hydrolase Inhibitors Solely Based on a Set of Simple Fragmental Descriptors.

BACKGROUND: The Soluble Epoxide Hydrolase (sEH) is a ubiquitously expressed enzyme in various tissues. The inhibition of the sEH has shown promising results to treat hypertension, alleviate pain and inflammation. OBJECTIVE: In this study, the power of machine learning has been employed to develop a predictive QSAR model for a large set of sEH inhibitors. METHODS: In this study, the random forest method was employed to make a valid model for the prediction of sEH inhibition. Besides, two new methods (Treeinterpreter python package and LIME, Local Interpretable Model-agnostic Explanations) have been exploited to explain and interpret the model. RESULTS: The performance metrics of the model were as follows: R2=0.831, Q2=0.565, RMSE=0.552 and R2 pred=0.595. The model also demonstrated good predictability on the two extra external test sets at least in terms of ranking. The Spearman's rank correlation coefficients for external test set 1 and 2 were 0.872 and 0.673, respectively. The external test set 2 was a diverse one compared to the training set. Therefore, the model could be used for virtual screening to enrich potential sEH inhibitors among a diverse compound library. CONCLUSION: As the model was solely developed based on a set of simple fragmental descriptors, the model was explained by two local interpretation algorithms, and this could guide medicinal chemists to design new sEH inhibitors. Moreover, the most important general descriptors (fragments) suggested by the model were consistent with the available crystallographic data. The model is available as an executable binary at http://www.pharm-sbg.com and https://github.com/shamsaraj.

Homology Modeling of 5-alpha-Reductase 2 Using Available Experimental Data.

5-Alpha-reductase 2 is an interesting pharmaceutical target for the treatment of several diseases, including prostate cancer, benign prostatic hyperplasia, male pattern baldness, acne, and hirsutism. One of the main approaches in computer aided drug design is structure-based drug discovery. However, the experimental 3D structure of 5-alpha-reductase 2 is not available at present. Therefore, a homology modeling method and molecular dynamics simulation were used to develop a reliable model of 5-alpha-reductase 2 for inhibitor pose prediction and virtual screening. Despite the low sequence identity between the target and template sequences, a useful 3D model of 5-alpha-reductase 2 was generated by the inclusion of experimental data.

Identification of Non-Zinc Binding Inhibitors of MMP-2 Through Virtual Screening and Subsequent Rescoring.

MMP-2 belongs to a large family of proteases called matrix metalloproteinases (MMPs) that degrades type IV collagen, the main structural component of basement membranes and gelatin. The main pathologic role of MMP-2 overexpression is to contribute to the development of cancer through the progression of metastasis and angiogenesis. A structure-based virtual screening was employed to find new inhibitors with possible selectivity for MMP-2. The inhibitory activities of 3 inhibitors (one was not a suitable drug-like hit) among 19 purchased compounds were approved by enzyme inhibition assay. 5 hits were non-zinc-binding inhibitors of MMP-2. The results demonstrated that a computer-aided drug design could be successfully applied for discovering new MMP-2 inhibitors. We found inhibitors with new scaffolds for the inhibition of MMP-2 with some selectivity features that could be used for future lead optimization processes. According to the docked pose and MD simulation, compound 13 was expected to interact with the S1' specificity loop of MMP-2 and had 2 π-π interactions and a stable hydrogen bond with the MMP-2 active site. The key feature of compound 13 could be used to guide the design of new non-zinc-binding inhibitors of MMP-2.

Correlation between Virtual Screening Performance and Binding Site Descriptors of Protein Targets.

Rescoring is a simple approach that theoretically could improve the original docking results. In this study AutoDock Vina was used as a docked engine and three other scoring functions besides the original scoring function, Vina, as well as their combinations as consensus scoring functions were employed to explore the effect of rescoring on virtual screenings that had been done on diverse targets. Rescoring by DrugScore produces the most number of cases with significant changes in screening power. Thus, the DrugScore results were used to build a simple model based on two binding site descriptors that could predict possible improvement by DrugScore rescoring. Furthermore, generally the screening power of all rescoring approach as well as original AutoDock Vina docking results correlated with the Maximum Theoretical Shape Complementarity (MTSC) and Maximum Distance from Center of Mass and all Alpha spheres (MDCMA). Therefore, it was suggested that, with a more complete set of binding site descriptors, it could be possible to find robust relationship between binding site descriptors and response to certain molecular docking programs and scoring functions. The results could be helpful for future researches aiming to do a virtual screening using AutoDock Vina and/or rescoring using DrugScore.

An integrated structure- and pharmacophore-based MMP-12 virtual screening.

MMP-12 belongs to a large family of proteases called matrix metalloproteinases (MMPs) that degrades elastin. The main pathologic role of MMP-12 overexpression was suggested to be associated with pathogenesis mechanism of inflammatory respiratory diseases and atherosclerosis. An integrated ligand- and structure-based virtual screening was employed in hope of finding inhibitors with new scaffolds and selectivity for MMP-12. Seven compounds among 18 experimentally tested compounds had a measurable effect on the inhibition of MMP-12 enzyme. Our results demonstrated the applicability of the developed pharmacophore model and selected crystal structure (PDB code: 3F17) to discover new MMP-12 inhibitors. The receptor structure was selected based on cross-docking results. Here, we report the discovery of new class of MMP-12 inhibitors that could be used for lead optimization. For the inhibition of MMP-12, the significance of its interactions with the catalytic residues Glu219 and Ala182 was emphasized through the inspection of the docking poses.

Virtual Screening on MMP-13 Led to Discovering New Inhibitors Including a Non-Zinc Binding and a Micro Molar One: A Successful Example of Receptor Selection According to Cross-Docking Results for a Flexible Enzyme.

AIM AND OBJECTIVE: MMP-13 belongs to a large family of proteases called matrix metalloproteinases (MMPs) that degrades type II collagen, the main structural protein of articular cartilage. The main pathologic role of MMP-13 over expression is to contribute to the development of osteoarthritis. METHODS: To find new inhibitors with possible selectivity for MMP-13 a structure based virtual screening was employed. The inhibitory activities of 11 inhibitors among 19 purchased compounds were approved by enzyme inhibition assay. RESULTS: Our results demonstrated that the CADD (computer aided drug design) could be successfully applied to find new MMP-13 inhibitors using a receptor structure (PDB code: 3O2X) which had been demonstrated a good performance in a cross-docking study. CONCLUSION: We discovered inhibitors with new scaffolds for inhibition of MMP-13 and some selectivity features such as proper S1' occupancy and interactions with S1' pocket that could be subjected to a future lead optimization study.

Qualitative Estimation of Drug Entrapment Efficiency in Polymeric Nano - Micelles Using Dissipative Particle Dynamics (DPD).

BACKGROUND: Dissipative particle dynamics (DPD) is a simulation method that has one of its applications in the field of pharmaceutical science and drug delivery. OBJECTIVE: DPD is employed to study morphology and some other characteristics of polymeric nanomicelles. Two systems were considered in this study: system A which includes curcumin, Polycaprolactone (PCL), Polyethylene glycol (PEG) and water beads and system B which includes paclitaxel, Polylactic acid (PLA), PEG and water beads. METHOD: In this study we focused on the simulation of drug entrapment in polymeric micelles using DPD method. RESULTS: Results indicated that the qualitative comparison of polymeric-micelles with different compositions, after carefully tuning input parameters and simulation conditions, can be successfully performed using DPD. CONCLUSION: Considering real state of a system for DPD simulation will have a great impact on the reliability of the simulation results.

Docking and QSAR Studies of 1,4-Dihydropyridine Derivatives as Anti- Cancer Agent.

BACKGROUND: The multidrug resistance (MDR) of cancer cells has become a great barrier to the success of chemotherapy. OBJECTIVE: In this study, quantitative structure activity relationship (QSAR) modeling was applied to 46 1,4-dihydropyridine structures (DHPs), and some selected compounds were docked. METHODS: QSAR was used to generate models and predict the MDR inhibitory activity for a series of 1,4-dihydropyridines (DHP). The DHPs were built and optimized using the Sybyl program (x1.2 version). Descriptor generation was done by DRAGON package. Docking was carried out using Auto Dock 4.2 software. Multiple linear regression, and partial least square were performed as QSAR modelgeneration methods. External validation, cross-validation (leave one out) and y-randomization were used as validation methods. RESULTS: The constructed model using stepwise-MLR and GA-PLS revealed good statistical parameters. In the final step all compounds were divided into two parts: symmetric (PLS) and asymmetric (MLR) 1,4-dihydropyridines and two other models were built. The square correlation coefficient (R2) and root mean square error (RMSE) for train set for GA-PLS were (R2 = 0.734, RMSE train = 0.26). CONCLUSION: The predictive ability of the models was found to be satisfactory and could be employed for designing new 1,4-dihydropyridines as potent MDR inhibitors in cancer treatment. 1,4- Dihydropyridine ring containing protonable nitrogen as scaffold could be proposed. Sulfur, ester, amide, acyle, ether, fragments are connected to a 1,4-dihydropyridine ring. Phenyl groups (with an electronegative substituent) as a lipophilic part are essential for the inhibitory effect.

Ezqsar: An R Package for Developing QSAR Models Directly From Structures.

BACKGROUND: Quantitative Structure Activity Relationship (QSAR) is a difficult computational chemistry approach for beginner scientists and a time consuming one for even more experienced researchers. METHOD AND MATERIALS: Ezqsar which is introduced here addresses both the issues. It considers important steps to have a reliable QSAR model. Besides calculation of descriptors using CDK library, highly correlated descriptors are removed, a provided data set is divided to train and test sets, descriptors are selected by a statistical method, statistical parameter for the model are presented and applicability domain is investigated. RESULTS: Finally, the model can be applied to predict the activities for an extra set of molecules for a purpose of either lead optimization or virtual screening. The performance is demonstrated by an example. CONCLUSION: The R package, ezqsar, is freely available via https://github.com/shamsaraj/ezqsar, and it runs on Linux and MS-Windows.

Developing a CoMSIA Model for Inhibition of COX-2 by Resveratrol Derivatives.

Design of selective cyclooxygenase-2 (COX-2) inhibitors is still a challenging task because of active site similarities between COX isoenzymes. To help with this issue, we tried to generate a 3D-QSAR (3 dimensional quantitative structure activity relationships) model that might reflect the essential features of COX-2 active sites. Compounds in a series of resveratrol derivatives inhibitors with reported biological activity against COX-2 were used to construct a predictive comparative molecular similarity indices (CoMSIA) model. A CoMSIA model with acceptable internal and external predictability was developed and employed to design new not yet synthesized molecules with improved activity and selectivity toward COX-2. Finally, molecular docking of the inhibitors in COX-2 active site demonstrated the possible ability of proposed compounds to inhibit COX-2, selectively.

CrossDocker: a tool for performing cross-docking using Autodock Vina.

BACKGROUND: Cross-docking is an approach to find the best holo structures among multiple structures available for a target protein. RESULTS: CrossDocker significantly decreases the time needed for setting parameters and inputs for performing multiple dockings, data collection and subsequent analysis. CONCLUSION: CrossDocker was written in Python language and is available as executable binary for Windows operating system. It is available at http://www.pharm-sbg.com. Some example data sets were also provided.

Optimal control therapy and vaccination for special HIV-1 model with delay.

In this paper, we consider a four dimensional model of the human immunodeficiency virus-1 (HIV-1) with delay, which is an extension of some three dimensional models. We approach the treatment problem by adding two controllers to the system for inhibiting viral production. The optimal controller [Formula: see text] is considered for vaccine and [Formula: see text] for the drug. The Pontryagin maximum principle with delay is used to characterize these optimal controls. At the end, numerical results are presented to illustrate the optimal solutions. The validity of the model was confirmed by proper semi-quantitative simulation of some clinical data. The model was used to predict the possible beneficial effects of vaccine and anti-retroviral drug administration in HIV-1 disease.

Considering Rotatability of Hydroxyl Groups for the Active Site Residues of MMP-13 in Retrospective Virtual Screening Campaigns.

Considering different orientation of hydroxyl and thiol groups of receptor residues such as Thr, Tyr, Ser and Cys is an option available on Glide docking software. This is an attempt that can provide more realistic ligand-receptor interactions. Matrix metalloproteinase 13 (MMP-13) is a suggested target for several diseases including osteoarthritis and cancer. MMP-13 was selected as a receptor with reported flexibility in the active site residues. Four residues in the MMP-13 active site were selected and their hydroxyl groups were made flexible during docking: Tyr(241), Thr(242), Tyr(243) and Thr(244). The ability of retrospective virtual screenings using a rigid receptor for discriminating between actives and decoys were compared to those using receptor with different combination of flexible residues. Statistical analysis of the results and inspecting the binding pose of the ligands suggested that the hydroxyl orientation of Tyr(241), Thr(242), Tyr(243) and Thr(244) (in particular Thr(242) and to a lesser extent Thr(244)) had impacts on the MMP-13 docking results.

Application of DPD in the design of polymeric nano-micelles as drug carriers.

Developing new drug carrier systems are of a great importance in the treatment approach for a wide range of diseases. The simulation techniques can be valuable for decreasing the time and cost of developing novel drug carriers. Among the simulation methods there are a vast number of studies using dissipative particle dynamics (DPD) method for the prediction of different aspects of polymeric nano-micelles for encapsulating drugs. Here, we reviewed the results of the studies employing DPD for the simulation of drug loading and release in different polymeric micelles carriers. In some cases the simulation results were compared with the experimental results by the authors that were demonstrated the reliability of the DPD predictions.