Melatonin Therapy Modulates Cerebral Metabolism and Enhances Remyelination by Increasing PDK4 in a Mouse Model of Multiple Sclerosis

Metabolic disturbances have been implicated in demyelinating diseases including multiple sclerosis (MS). Melatonin, a naturally occurring hormone, has emerged as a potent neuroprotective candidate to reduce myelin loss and improve MS outcomes. In this study, we evaluated the effect of melatonin, at both physiological and pharmacological doses, on oligodendrocytes metabolism in an experimental autoimmune encephalomyelitis (EAE) mouse model of MS. Results showed that melatonin decreased neurological disability scores and enhanced remyelination, significantly increasing myelin protein levels including MBP, MOG, and MOBP. In addition, melatonin attenuated inflammation by reducing pro-inflammatory cytokines (IL-1β and TNF-α) and increasing anti-inflammatory cytokines (IL-4 and IL-10). Moreover, melatonin significantly increased brain concentrations of lactate, N-acetylaspartate (NAA), and 3-hydroxy-3-methylglutaryl-coenzyme-A reductase (HMGCR). Pyruvate dehydrogenase kinase-4 (PDK-4) mRNA and protein expression levels were also increased in melatonin-treated, compared to untreated EAE mice. However, melatonin significantly inhibited active and total pyruvate dehydrogenase complex (PDC), an enzyme under the control of PDK4. In summary, although PDC activity was reduced by melatonin, it caused a reduction in inflammatory mediators while stimulating oligodendrogenesis, suggesting that oligodendrocytes are forced to use an alternative pathway to synthesize fatty acids for remyelination. We propose that combining melatonin and PDK inhibitors may provide greater benefits for MS patients than the use of melatonin therapy alone

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. Graphical abstract: [Figure not available: see fulltext.]Display graphical of the hybrid model of Matrix Factorization with Denoising Autoencoderswith 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

Moonlighting protein prediction using physico-chemical and evolutional properties via machine learning methods.

BACKGROUND: Moonlighting proteins (MPs) are a subclass of multifunctional proteins in which more than one independent or usually distinct function occurs in a single polypeptide chain. Identification of unknown cellular processes, understanding novel protein mechanisms, improving the prediction of protein functions, and gaining information about protein evolution are the main reasons to study MPs. They also play an important role in disease pathways and drug-target discovery. Since detecting MPs experimentally is quite a challenge, most of them are detected randomly. Therefore, introducing an appropriate computational approach to predict MPs seems reasonable. RESULTS: In this study, we introduced a competent model for detecting moonlighting and non-MPs through extracted features from protein sequences. We attempted to set up a well-judged scheme for detecting outlier proteins. Consequently, 37 distinct feature vectors were utilized to study each proteins impact on detecting MPs. Furthermore, 8 different classification methods were assessed to find the best performance. To detect outliers, each one of the classifications was executed 100 times by tenfold cross-validation on feature vectors; proteins which misclassified 90 times or more were grouped. This process was applied to every single feature vector and eventually the intersection of these groups was determined as the outlier proteins. The results of tenfold cross-validation on a dataset of 351 samples (containing 215 moonlighting and 136 non-moonlighting proteins) reveal that the SVM method on all feature vectors has the highest performance among all methods in this study and other available methods. Besides, the study of outliers showed that 57 of 351 proteins in the dataset could be an appropriate candidate for the outlier. Among the outlier proteins, there were non-MPs (such as P69797) that have been misclassified in 8 different classification methods with 16 different feature vectors. Because these proteins have been obtained by computational methods, the results of this study could reduce the likelihood of hypothesizing whether these proteins are non-moonlighting at all. CONCLUSIONS: MPs are difficult to be identified through experimentation. Using distinct feature vectors, our method enabled identification of novel moonlighting proteins. The study also pinpointed that a number of non-MPs are likely to be moonlighting

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

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

Abstract 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

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

Abstract 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

Multi experimental and computational studies for DNA and HSA interaction of new nano-scale ultrasound-assisted synthesized Pd(II) complex as a potent anticancer drug

International audienceAs for daily increasing mortality rate in world due to the growth of cancer causing agents, design and synthesis of new compounds with anticancer potential benefits is one of the most important challenges for researchers. In the present work, we synthesized a new Schiff base Pd(II) complex in bulk-scale and also in nano-scales by Sonochemical method. The structure of synthesized complex was determined by single crystal X-ray diffraction technique. Then the cell viability percent of HeLa cancer cells was studied by MTT assay. The results confirmed that reducing the size has salient effect in annihilation of cancer cells. Also, nano-scale complex reached to IC50 in 10 μM of concentration. Binding ability of the nano- and bulk-scale Pd(II) Schiff base complex with calf thymus DNA and human serum albumin was investigated using combination of experimental (fluorescence, circular dichroism (CD) and viscosity) and computational (molecular docking, molecular dynamics simulation and QM/MM) methods. The estimated binding constants for the complex in both of bulk- and nano-scales showed that the nano-scale complex binds more tightly to DNA than its bulk-scale form. This finding is in good agreement with MTT assay results. Molecular docking studies revealed that Pd(II) complex binds to the minor groove and IB binding site of DNA and HSA, respectively. Also, MD simulation studies showed that complexation with the Pd(II) complex changes the structure of HSA with compared to free protein. Finally, the ONIOM results indicated that the structural parameters of the compound changed along with binding to DNA and HSA, indicating the strong interaction between the compound and these biomacromolecules. The values of binding constants depend on the extent of the resultant changes