Computational evaluation and benchmark study of 342 crystallographic holo-structures of SARS-CoV-2 Mpro enzyme.

The coronavirus disease 19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to a global health crisis with millions of confirmed cases and related deaths. The main protease (Mpro) of SARS-CoV-2 is crucial for viral replication and presents an attractive target for drug development. Despite the approval of some drugs, the search for effective treatments continues. In this study, we systematically evaluated 342 holo-crystal structures of Mpro to identify optimal conformations for structure-based virtual screening (SBVS). Our analysis revealed limited structural flexibility among the structures. Three docking programs, AutoDock Vina, rDock, and Glide were employed to assess the efficiency of virtual screening, revealing diverse performances across selected Mpro structures. We found that the structures 5RHE, 7DDC, and 7DPU (PDB Ids) consistently displayed the lowest EF, AUC, and BEDROCK scores. Furthermore, these structures demonstrated the worst pose prediction results in all docking programs. Two structural differences contribute to variations in docking performance: the absence of the S1 subsite in 7DDC and 7DPU, and the presence of a subpocket in the S2 subsite of 7DDC, 7DPU, and 5RHE. These findings underscore the importance of selecting appropriate Mpro conformations for SBVS, providing valuable insights for advancing drug discovery efforts.

Evaluation of antibacterial, cytotoxicity, and apoptosis activity of novel chromene-sulfonamide hybrids synthesized under solvent-free conditions and 3D-QSAR modeling studies.

In this study, eleven novel chromene sulfonamide hybrids were synthesized by a convenient method in accordance with green chemistry. At first, chromene derivatives (1-9a) were prepared through the multi-component reaction between aryl aldehydes, malononitrile, and 3-aminophenol. Then, synthesized chromenes were reacted with appropriate sulfonyl chlorides by grinding method to give the corresponding chromene sulfonamide hybrids (1-11b). Synthesized hybrids were obtained in good to high yield and characterized by IR, 1HNMR, 13CNMR, CHN and melting point techniques. In addition, the broth microdilution assay was used to determine the minimal inhibitory concentration of newly synthesized chromene-sulfonamide hybrids. The MTT test was used to determine the cytotoxicity and apoptotic activity of the newly synthesized compounds against fibroblast L929 cells. The 3D­QSAR analysis confirmed the experimental assays, demonstrating that our predictive model is useful for developing new antibacterial inhibitors. Consequently, molecular docking studies were performed to validate the findings of the 3D-QSAR analysis, confirming the potential binding interactions of the synthesized chromene-sulfonamide hybrids with the target enzymes. Molecular docking studies were employed to support the 3D-QSAR predictions, providing insights into the binding interactions between the newly synthesized chromene-sulfonamide hybrids and their target bacterial enzymes, thereby reinforcing the potential efficacy of these compounds as antibacterial agents. Also, some of the experimental outcomes supported or conflicted with the pharmacokinetic prediction (especially about compound carcinogenicity). The performance of ADMET predictor results was assessed. The work presented here proposes a computationally driven strategy for designing and discovering a new sulfonamide scaffold for bacterial inhibition.

A comparative analysis of computational drug repurposing approaches: proposing a novel tensor-matrix-tensor factorization method.

Efficient drug discovery relies on drug repurposing, an important and open research field. This work presents a novel factorization method and a practical comparison of different approaches for drug repurposing. First, we propose a novel tensor-matrix-tensor (TMT) formulation as a new data array method with a gradient-based factorization procedure. Additionally, this paper examines and contrasts four computational drug repurposing approaches-factorization-based methods, machine learning methods, deep learning methods, and graph neural networks-to fulfill the second purpose. We test the strategies on two datasets and assess each approach's performance, drawbacks, problems, and benefits based on results. The results demonstrate that deep learning techniques work better than other strategies and that their results might be more reliable. Ultimately, graph neural methods need to be in an inductive manner to have a reliable prediction.

CCL-DTI: contributing the contrastive loss in drug-target interaction prediction.

BACKGROUND: The Drug-Target Interaction (DTI) prediction uses a drug molecule and a protein sequence as inputs to predict the binding affinity value. In recent years, deep learning-based models have gotten more attention. These methods have two modules: the feature extraction module and the task prediction module. In most deep learning-based approaches, a simple task prediction loss (i.e., categorical cross entropy for the classification task and mean squared error for the regression task) is used to learn the model. In machine learning, contrastive-based loss functions are developed to learn more discriminative feature space. In a deep learning-based model, extracting more discriminative feature space leads to performance improvement for the task prediction module. RESULTS: In this paper, we have used multimodal knowledge as input and proposed an attention-based fusion technique to combine this knowledge. Also, we investigate how utilizing contrastive loss function along the task prediction loss could help the approach to learn a more powerful model. Four contrastive loss functions are considered: (1) max-margin contrastive loss function, (2) triplet loss function, (3) Multi-class N-pair Loss Objective, and (4) NT-Xent loss function. The proposed model is evaluated using four well-known datasets: Wang et al. dataset, Luo's dataset, Davis, and KIBA datasets. CONCLUSIONS: Accordingly, after reviewing the state-of-the-art methods, we developed a multimodal feature extraction network by combining protein sequences and drug molecules, along with protein-protein interaction networks and drug-drug interaction networks. The results show it performs significantly better than the comparable state-of-the-art approaches.

A Molecular Generative Model of COVID-19 Main Protease Inhibitors Using Long Short-Term Memory-Based Recurrent Neural Network.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a serious threat to public health and prompted researchers to find anti-coronavirus 2019 (COVID-19) compounds. In this study, the long short-term memory-based recurrent neural network was used to generate new inhibitors for the coronavirus. First, the model was trained to generate drug compounds in the form of valid simplified molecular-input line-entry system strings. Then, the structures of COVID-19 main protease inhibitors were applied to fine-tune the model. After fine-tuning, the network could generate new molecular structures as novel SARS-CoV-2 main protease inhibitors. Molecular docking exhibited that some generated compounds have the proper affinity to the active site of the protease. Molecular Dynamics simulations explored binding free energies of the compounds over simulation trajectories. In addition, in silico absorption, distribution, metabolism, and excretion studies showed that some novel compounds could be formulated as orally active agents. Based on molecular docking and molecular dynamics simulation studies, compound AADH possessed significant binding affinity and presumably inhibition against the SARS-CoV-2 main protease enzyme. Therefore, the proposed deep learning-based model was capable of generating promising anti-COVID-19 drugs.

In silico studies of anti-oxidative and hot temperament-based phytochemicals as natural inhibitors of SARS-CoV-2 Mpro.

Main protease (Mpro) of SARS-CoV-2 is considered one of the key targets due to its role in viral replication. The use of traditional phytochemicals is an important part of complementary/alternative medicine, which also accompany the concept of temperament, where it has been shown that hot medicines cure cold and cold medicines cure hot, with cold and hot pattern being associated with oxidative and anti-oxidative properties in medicine, respectively. Molecular docking in this study has demonstrated that a number of anti-oxidative and hot temperament-based phytochemicals have high binding affinities to SARS-CoV-2 Mpro, both in the monomeric and dimeric deposited states of the protein. The highest ranking phytochemicals identified in this study included savinin, betulinic acid and curcumin. Complexes of savinin, betulinic acid, curcumin as well as Nirmatrelvir (the only approved inhibitor, used for comparison) bound to SARS-CoV-2 Mpro were further subjected to molecular dynamics simulations. Subsequently, RMSD, RMSF, Rg, number of hydrogen bonds, binding free energies and residue contributions (using MM-PBSA) and buried surface area (BSA), were analysed. The computational results suggested high binding affinities of savinin, betulinic acid and curcumin to both the monomeric and dimeric deposited states of Mpro, while highlighting the lower binding energy of betulinic acid in comparison with savinin and curcumin and even Nirmatrelvir, leading to a greater stability of the betulinic acid-SARS-CoV-2 Mpro complex. Overall, based on the increasing mutation rate in the spike protein and the fact that the SARS-CoV-2 Mpro remains highly conserved, this study provides an insight into the use of phytochemicals against COVID-19 and other coronavirus diseases.

DEDTI versus IEDTI: efficient and predictive models of drug-target interactions.

Drug repurposing is an active area of research that aims to decrease the cost and time of drug development. Most of those efforts are primarily concerned with the prediction of drug-target interactions. Many evaluation models, from matrix factorization to more cutting-edge deep neural networks, have come to the scene to identify such relations. Some predictive models are devoted to the prediction's quality, and others are devoted to the efficiency of the predictive models, e.g., embedding generation. In this work, we propose new representations of drugs and targets useful for more prediction and analysis. Using these representations, we propose two inductive, deep network models of IEDTI and DEDTI for drug-target interaction prediction. Both of them use the accumulation of new representations. The IEDTI takes advantage of triplet and maps the input accumulated similarity features into meaningful embedding corresponding vectors. Then, it applies a deep predictive model to each drug-target pair to evaluate their interaction. The DEDTI directly uses the accumulated similarity feature vectors of drugs and targets and applies a predictive model on each pair to identify their interactions. We have done a comprehensive simulation on the DTINet dataset as well as gold standard datasets, and the results show that DEDTI outperforms IEDTI and the state-of-the-art models. In addition, we conduct a docking study on new predicted interactions between two drug-target pairs, and the results confirm acceptable drug-target binding affinity between both predicted pairs.

New insights into the inhibitory effect of phenol carboxylic acid antioxidants on mushroom tyrosinase by molecular dynamic studies and experimental assessment.

The inhibitory effects of ferulic and chlorogenic acids on tyrosinase activity were investigated through multi-spectroscopic and molecular docking techniques. Ferulic and chlorogenic acids, flavonoid compounds, demonstrated inhibitory monophenolase activities of tyrosinase. The inhibitor effects against monophenolase activity were in a reversible and competitive manner with ki value equal to 6.8 and 7.5 µM respectively. The affinity between tyrosinase and L-DOPA decreased when fatty acids were added to the solution. The multi-spectroscopic techniques like UV-vis, fluorescence, and isothermal calorimetry are employed to investigate changes. Intrinsic fluorescence quenching and conformational changes of tyrosinase by hydrophobic interaction were confirmed. Tyrosinase had two and three binding sites for ferulic and chlorogenic acids with a binding constant in the order of magnitude of -6.8 and -7.2 kcal/mol. In addition, the secondary structural changes with Circular dichroism (CD) analysis, secondary structure (DSSP), radius of gyration (Rg) and analysis of hydrogen bonds (H-bonds) confirmed. Ferulic acid effect can be observed obviously and also content of α-helix decreased. Thermodynamic parameters indicated that the interaction between enzyme and ferulic and chlorogenic acids followed a spontaneous reaction dynamic manner with ΔG = -14.78 kJ/mol and ΔG = -14.61 kJ/mol (298k). The findings highlighted the potential applications of ferulic acid and chlorogenic acids in food and drug industries as potent inhibitors of tyrosinase.Communicated by Ramaswamy H. Sarma.

In silico study Ferulic and Chlorogenic Acids was performed to check the binding profile against tyrosinase.Investigate the inhibitory It inhibited tyrosinase in a competitive manner.Ferulic and Chlorogenic fatty acids for prevention of medical hyperpigmentation, and it is a good candidate for cosmetic applications.

Virtual screening reveals aprepitant to be a potent inhibitor of neutral sphingomyelinase 2: implications in blockade of exosome release in cancer therapy.

PURPOSE: Exosomes are membrane-derived nano-vesicles upregulated in pathological conditions like cancer. Therefore, inhibiting their release is a potential strategy for the development of more efficient combination therapies. Neutral sphingomyelinase 2 (nSMase2) is a key component in exosome release; however, a clinically safe yet efficient nSMase2 inhibitor remains to be used discovered. Accordingly, we made an effort to identify potential nSMase2 inhibitor(s) among the approved drugs. METHODS: Virtual screening was performed and aprepitant was selected for further investigation. To evaluate the reliability of the complex, molecular dynamics were performed. Finally, using the CCK-8 assay in HCT116 cells, the highest non-toxic concentrations of aprepitant were identified and the nSMase2 activity assay was performed to measure the inhibitory activity of aprepitant, in vitro. RESULTS: To validate the screening results, molecular docking was performed, and the retrieved scores were in line with the screening results. The root-mean-square deviation (RMSD) plot of aprepitant-nSMase2 showed proper convergence. Following treatment with different concentrations of aprepitant in both cell-free and cell-dependent assays, nSMase2 activity was remarkably decreased. CONCLUSION: Aprepitant, at a concentration as low as 15 µM, was able to inhibit nSmase2 activity in HCT116 cells without any significant effects on their viability. Aprepitant is therefore suggested to be a potentially safe exosome release inhibitor.

Rational approaches to discover SARS-CoV-2/ACE2 interaction inhibitors: Pharmacophore-based virtual screening, molecular docking, molecular dynamics and binding free energy studies.

The lack of effective treatment remains a bottleneck in combating the current coronavirus family pandemic, particularly coronavirus 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The infection of host cells by SARS-CoV-2 is mediated by the binding of its receptor-binding domain (RBD) on the spike (S) glycoprotein to the host angiotensin-converting enzyme (ACE2) receptor. As all developed and available vaccines against COVID-19 do not provide long-term immunity, the creation of an effective drug for the treatment of COVID-19 is necessary and cannot be ignored. Therefore, the aim of this study is to present a computational screening method to identify potential inhibitor candidates with a high probability of blocking the binding of RBD to the ACE2 receptor. Pharmacophore mapping, molecular docking, molecular dynamics (MD) simulations, and binding free-energy analyses were performed to identify potential inhibitor candidates against ACE2/SARS-CoV-2. In conclusion, we propose the compound PubChem-84280085 as a potential inhibitor of protein-protein interactions to disrupt the binding of the SARS-CoV-2-RBD to the ACE2 receptor.

DRaW: prediction of COVID-19 antivirals by deep learning-an objection on using matrix factorization.

BACKGROUND: Due to the high resource consumption of introducing a new drug, drug repurposing plays an essential role in drug discovery. To do this, researchers examine the current drug-target interaction (DTI) to predict new interactions for the approved drugs. Matrix factorization methods have much attention and utilization in DTIs. However, they suffer from some drawbacks. METHODS: We explain why matrix factorization is not the best for DTI prediction. Then, we propose a deep learning model (DRaW) to predict DTIs without having input data leakage. We compare our model with several matrix factorization methods and a deep model on three COVID-19 datasets. In addition, to ensure the validation of DRaW, we evaluate it on benchmark datasets. Furthermore, as an external validation, we conduct a docking study on the COVID-19 recommended drugs. RESULTS: In all cases, the results confirm that DRaW outperforms matrix factorization and deep models. The docking results approve the top-ranked recommended drugs for COVID-19. CONCLUSIONS: In this paper, we show that it may not be the best choice to use matrix factorization in the DTI prediction. Matrix factorization methods suffer from some intrinsic issues, e.g., sparsity in the domain of bioinformatics applications and fixed-unchanged size of the matrix-related paradigm. Therefore, we propose an alternative method (DRaW) that uses feature vectors rather than matrix factorization and demonstrates better performance than other famous methods on three COVID-19 and four benchmark datasets.

Proteochemometrics modeling for prediction of the interactions between caspase isoforms and their inhibitors.

Caspases (cysteine-aspartic proteases) play critical roles in inflammation and the programming of cell death in the form of necroptosis, apoptosis, and pyroptosis. The name of these enzymes has been chosen in accordance with their cysteine protease activity. They act as cysteines in nucleophilically active sites to attack and cleave target proteins in the aspartic acid and amino acid C-terminal. Based on the substrate's structure and the specificity, the physiological activity of caspases is divided. However, in apoptosis, the division of caspases into initiating caspases (caspase 2, 8, 9, and 10) and executive caspases (caspase 3, 6, and 7) is essential. The present study aimed to perform Proteochemometrics Modeling to generalize the data on caspases, which could predict ligand and protein interactions. In this study, we employed protein and ligand descriptors. Moreover, protein descriptors were computed using the Protr R package, while PADEL-Descriptor was employed for the computation of ligand descriptors. In addition, NCA (Neighborhood Component Analyses) was used for descriptor selection, and SVR, decision tree, and ensemble methods were utilized for the proteochemometrics modeling. This study shows that the ensemble model demonstrates superior performance compared with other models in terms of R2, Q2, and RMSE criteria.

Biomolecular interactions and binding dynamics of inhibitor arachidonic acid, with tyrosinase enzyme.

Hyperpigmentation is a disorder caused by increased melanin deposition and changes in skin pigmentation. Inhibition of tyrosinase activity contributes to the control of food browning and skin pigmentation diseases. The effects of arachidonic acid (AA) on tyrosinase activity were examined using different spectroscopy methods including UV-VIS spectrophotometry, fluorescence spectroscopy, circular dichroism (CD) differential scanning calorimetry, and molecular dynamics (MD) simulations. Based on the kinetic results, arachidonic acid showed mixed-type of inhibition with Ki = 4.7 µM. Fluorescence and CD studies showed changes of secondary and tertiary structures of enzyme and a reduction of α-helix* amino acids after its incubation with different concentrations of AA, which is also confirmed by DSSP analysis. In addition, differential scanning calorimetry (DSC) studies showed a decrease in thermodynamic stability of enzyme from Tm = 338.65k for sole enzyme after incubation with AA in comparison with complex enzyme with Tm= 334.26k, ΔH =7.52 kJ/mol, and ΔS = 0.15 kJ/mol k. Based on the theoretical methods, it was found that the interaction between enzyme and AA follows an electrostatic manner with ΔG = -8.314 kJ/mol and ΔH = -12.9 kJ/mol. The MD results showed the lowest flexibility in the complex amino acids and minimal fluctuations in AA interaction with tyrosinase in Residue 240 to 260 and 66 to 80. Thus, AA inhibitory and structural and thermodynamic instability of tyrosinase supported advantages of this fatty acid for prevention of medical hyperpigmentation. Therefore, it is a good candidate for cosmetic applications. Communicated by Ramaswamy H. Sarma.

Identification of new potent agonists for toll-like receptor 8 by virtual screening methods, molecular dynamics simulation, and MM-GBSA.

Toll-like receptor 8 (TLR8), as an endosomal transmembrane receptor, plays a crucial role in the innate immune response to neoplasia and viruses. Previous studies have shown that TLR8 agonists e.g. Motolimod can be used to treat patients with last-stage cancer. In this study, in order to find new suitable ligands for TLR8, 16 PBD codes related to TLR8 complexes were collected to design the pharmacophore models using the Pharmit server. Then the PubChem, and ZINC databases were screened by them. Subsequently, the ADME-Tox features of the compounds were detected using FAF-Drugs4 and the selected compounds were docked to TLR8 (PDB: 3w3j). Molecular dynamics simulation was used to compare compounds with the best docking scores, with Motolimod in complex with TLR8. Finally, two compounds were identified, PubChem: 124126919 (A) and PubChem: 18559540 (B), each with advantages over Motolimod. As the RMSD results showed that compound A has very good flexibility, in terms of energy calculated using the MM-GBSA method, complex B and TLR8 showed the lowest energy level compared to the rest of the complexes. These observations suggest that these two compounds could be used as TLR8 agonists with the desired pharmacological features in future experimental studies.Communicated by Ramaswamy H. Sarma.

Matrix factorization with denoising autoencoders for prediction of drug-target interactions.

Drug-target interaction is crucial in the discovery of new drugs. Computational methods can be used to identify new drug-target interactions at low costs and with reasonable accuracy. Recent studies pay more attention to machine-learning methods, ranging from matrix factorization to deep learning, in the DTI prediction. Since the interaction matrix is often extremely sparse, DTI prediction performance is significantly decreased with matrix factorization-based methods. Therefore, some matrix factorization methods utilize side information to address both the sparsity issue of the interaction matrix and the cold-start issue. By combining matrix factorization and autoencoders, we propose a hybrid DTI prediction model that simultaneously learn the hidden factors of drugs and targets from their side information and interaction matrix. The proposed method is composed of two steps: the pre-processing of the interaction matrix, and the hybrid model. We leverage the similarity matrices of both drugs and targets to address the sparsity problem of the interaction matrix. The comparison of our approach against other algorithms on the same reference datasets has shown good results regarding area under receiver operating characteristic curve and the area under precision-recall curve. More specifically, experimental results achieve high accuracy on golden standard datasets (e.g., Nuclear Receptors, GPCRs, Ion Channels, and Enzymes) when performed with five repetitions of tenfold cross-validation. Display graphical of the hybrid model of Matrix Factorization with Denoising Autoencoders with the help side information of drugs and targets for Prediction of Drug-Target Interactions.

Insights on the conformation and appropriate drug-target sites on retinal IMPDH1 using the 604-aa isoform lacking the C-terminal extension.

Background and purpose: Retinitis pigmentosa (RP) accounts for 2 percent of global cases of blindness. The RP10 form of the disease results from mutations in isoform 1 of inosine 5'-monophosphate dehydrogenase (IMPDH1), the rate-limiting enzyme in the de novo purine nucleotide synthesis pathway. Retinal photoreceptors contain specific isoforms of IMPDH1 characterized by terminal extensions. Considering previously reported significantly varied kinetics among retinal isoforms, the current research aimed to investigate possible structural explanations and suitable functional sites for the pharmaceutical targeting of IMPDH1 in RP. Experimental approach: A recombinant 604-aa IMPDH1 isoform lacking the carboxyl-terminal peptide was produced and underwent proteolytic digestion with α-chymotrypsin. Dimer models of wild type and engineered 604-aa isoform were subjected to molecular dynamics simulation. Findings/Results: The IMPDH1 retinal isoform lacking C-terminal peptide was shown to tend to have more rapid proteolysis (~16% digestion in the first two minutes). Our computational data predicted the potential of the amino-terminal peptide to induce spontaneous inhibition of IMPDH1 by forming a novel helix in a GTP binding site. On the other hand, the C-terminal peptide might block the probable inhibitory role of the N-terminal extension. Conclusion and implications: According to the findings, augmenting IMPDH1 activity by suppressing its filamentation is suggested as a suitable strategy to compensate for its disrupted activity in RP. This needs specific small molecule inhibitors to target the filament assembly interface of the enzyme.

Identification of repurposed drugs targeting significant long non-coding RNAs in the cross-talk between diabetes mellitus and Alzheimer's disease.

The relationship between diabetes mellitus (DM) and Alzheimer's disease (AD) is so strong that scientists called it "brain diabetes". According to several studies, the critical factor in this relationship is brain insulin resistance. Due to the rapid global spread of both diseases, overcoming this cross-talk has a significant impact on societies. Long non-coding RNAs (lncRNAs), on the other hand, have a substantial impact on complex diseases due to their ability to influence gene expression via a variety of mechanisms. Consequently, the regulation of lncRNA expression in chronic diseases permits the development of innovative therapeutic techniques. However, developing a new drug requires considerable time and money. Recently repurposing existing drugs has gained popularity due to the use of low-risk compounds, which may result in cost and time savings. in this study, we identified drug repurposing candidates capable of controlling the expression of common lncRNAs in the cross-talk between DM and AD. We also utilized drugs that interfered with this cross-talk. To do this, high degree common lncRNAs were extracted from microRNA-lncRNA bipartite network. The drugs that interact with the specified lncRNAs were then collected from multiple data sources. These drugs, referred to as set D, were classified in to positive (D+) and negative (D-) groups based on their effects on the expression of the interacting lncRNAs. A feature selection algorithm was used to select six important features for D. Using a random forest classifier, these features were capable of classifying D+ and D- with an accuracy of 82.5%. Finally, the same six features were extracted for the most recently Food and Drug Administration (FDA) approved drugs in order to identify those with the highest likelihood of belonging to D+ or D-. The most significant FDA-approved positive drugs, chromium nicotinate and tapentadol, were presented as repurposing candidates, while cefepime and dihydro-alpha-ergocryptine were recommended as significant adverse drugs. Moreover, two natural compounds, curcumin and quercetin, were recommended to prevent this cross-talk. According to the previous studies, less attention has been paid to the role of lncRNAs in this cross-talk. Our research not only did identify important lncRNAs, but it also suggested potential repurposed drugs to control them.

Exploring the role of non-coding RNAs as potential candidate biomarkers in the cross-talk between diabetes mellitus and Alzheimer's disease.

Background: Recent research has investigated the connection between Diabetes Mellitus (DM) and Alzheimer's Disease (AD). Insulin resistance plays a crucial role in this interaction. Studies have focused on dysregulated proteins to disrupt this connection. Non-coding RNAs (ncRNAs), on the other hand, play an important role in the development of many diseases. They encode the majority of the human genome and regulate gene expression through a variety of mechanisms. Consequently, identifying significant ncRNAs and utilizing them as biomarkers could facilitate the early detection of this cross-talk. On the other hand, computational-based methods may help to understand the possible relationships between different molecules and conduct future wet laboratory experiments. Materials and methods: In this study, we retrieved Genome-Wide Association Study (GWAS, 2008) results from the United Kingdom Biobank database using the keywords "Alzheimer's" and "Diabetes Mellitus." After excluding low confidence variants, statistical analysis was performed, and adjusted p-values were determined. Using the Linkage Disequilibrium method, 127 significant shared Single Nucleotide Polymorphism (SNP) were chosen and the SNP-SNP interaction network was built. From this network, dense subgraphs were extracted as signatures. By mapping each signature to the reference genome, genes associated with the selected SNPs were retrieved. Then, protein-microRNA (miRNA) and miRNA-long non-coding RNA (lncRNA) bipartite networks were built and significant ncRNAs were extracted. After the validation process, by applying the scoring function, the final protein-miRNA-lncRNA tripartite network was constructed, and significant miRNAs and lncRNAs were identified. Results: Hsa-miR-199a-5p, hsa-miR-199b-5p, hsa-miR-423-5p, and hsa-miR-3184-5p, the four most significant miRNAs, as well as NEAT1, XIST, and KCNQ1OT1, the three most important lncRNAs, and their interacting proteins in the final tripartite network, have been proposed as new candidate biomarkers in the cross-talk between DM and AD. The literature review also validates the obtained ncRNAs. In addition, miRNA/lncRNA pairs; hsa-miR-124-3p/KCNQ1OT1, hsa-miR-124-3p/NEAT1, and hsa-miR-124-3p/XIST, all expressed in the brain, and their interacting proteins in our final network are suggested for future research investigation. Conclusion: This study identified 127 shared SNPs, 7 proteins, 15 miRNAs, and 11 lncRNAs involved in the cross-talk between DM and AD. Different network analysis and scoring function suggested the most significant miRNAs and lncRNAs as potential candidate biomarkers for wet laboratory experiments. Considering these candidate biomarkers may help in the early detection of DM and AD co-occurrence.

Drug-target interaction prediction using reliable negative samples and effective feature selection methods.

Machine learning-based approaches in the field of drug discovery have dramatically reduced the time and cost of the laboratory process of detecting potential drug-target interactions (DTIs). Standard binary classifiers require both positive and negative samples in the training and validation phases. One of the major challenges in the DTI context is the lack of access to non-interacting pairs as negative samples in the learning process. Many recent studies in this field have randomly selected negative samples from unlabeled drug-target pairs. Therefore, due to the probability of the presence of unknown positive samples in a set considered as negative samples, the model results may be affected and appear with a high rate of false positive. In this study, an algorithm called Reliable Non-Interacting Drug-Target Pairs (RNIDTP) is proposed to select reliable negative samples and an efficient algorithm to select relevant features for drug-target interaction prediction. To validate the performance of the proposed RNIDTP algorithm in the selection of negative samples, a benchmark drug-target interactions dataset is used. The results demonstrate the superiority of the proposed algorithm compared with other algorithms in most cases. The results also indicate that by using an appropriate algorithm for the selection of negative samples, the performance of the learning process is significantly increased compared to random selection.

Structural systems pharmacology: A framework for integrating metabolic network and structure-based virtual screening for drug discovery against bacteria.

Advances in genome-scale metabolic models (GEMs) and computational drug discovery have caused the identification of drug targets at the system-level and inhibitors to combat bacterial infection and drug resistance. Here we report a structural systems pharmacology framework that integrates the GEM and structure-based virtual screening (SBVS) method to identify drugs effective for Escherichia coli infection. The most complete genome-scale metabolic reconstruction integrated with protein structures (GEM-PRO) of E. coli, iML1515_GP, and FDA-approved drugs have been used. FBA was performed to predict drug targets in silico. The 195 essential genes were predicted in the rich medium. The subsystems in which a significant number of these genes are involved are cofactor, lipopolysaccharide (LPS) biosynthesis that are necessary for cell growth. Therefore, some proteins encoded by these genes are responsible for the biosynthesis and transport of LPS which is the first line of defense against threats. So, these proteins can be potential drug targets. The enzymes with experimental structure and cognate ligands were selected as final drug targets for performing the SBVS method. Finally, we have suggested those drugs that have good interaction with the selected proteins as drug repositioning cases. Also, the suggested molecules could be promising lead compounds. This framework may be helpful to fill the gap between genomics and drug discovery. Results show this framework suggests novel antibacterials that can be subjected to experimental testing soon and it can be suitable for other pathogens.