Computational profiling of pore properties of outer membrane proteins

Computational profiling of pore properties of outer membrane protein

Evaluation of anti-cancer effect of zerumbone and cisplatin on <i>N</i>-nitrosodiethylamine induced hepatic cancer in freshwater fish (<i>Danio rerio</i>)

Cancer is the uncontrolled proliferation of abnormal cells in the body. There is a foreseeable need for an effective anti-carcinogenic drug. In this regard, zerumbone (ZER) is identified as one such therapeutic herbal compound that has been shown to enhance the anticancer activity of cisplatin (CIS), with negligible side effects. Yet, the fundamental mechanisms of co-treatment of ZER and CIS on Hepatocellular carcinoma remain indefinable. The current study is endeavored to evaluate the anti-cancer effect of the individual and co-treatment of ZER, CIS and its combination on Diethyl nitrosamine induced hepatic cancer in wild-type zebra fish (Danio Rerio) models. Our careful analysis on treated and untreated fishes shows that CIS + ZER combination group restricted further progression of hepatocellular carcinoma cells significantly, which concludes that co-treatment of ZER with CIS was therapeutically effective for treating human HCC cancer cells which were induced into zebra fish.</p

Molecular modeling and dynamics studies of the synthetic small molecule agonists with GPR17 and P2Y1 receptor

The human Guanine Protein coupled membrane Receptor 17 (hGPR17), an orphan receptor that activates uracil nucleotides and cysteinyl leukotrienes is considered as a crucial target for the neurodegenerative diseases. Yet, the detailed molecular interaction of potential synthetic ligands of GPR17 needs to be characterized. Here, we have studied a comparative analysis on the interaction specificity of GPR17-ligands with hGPR17 and human purinergic G protein-coupled receptor (hP2Y1) receptors. Previously, we have simulated the interaction stability of synthetic ligands such as T0510.3657, AC1MLNKK, and MDL29951 with hGPR17 and hP2Y1 receptor in the lipid environment. In the present work, we have comparatively studied the protein-ligand interaction of hGPR17-T0510.3657 and P2Y1-MRS2500. Sequence analysis and structural superimposition of hGPR17 and hP2Y1 receptor revealed the similarities in the structural arrangement with the local backbone root mean square deviation (RMSD) value of 1.16 Å and global backbone RMSD value of 5.30 Å. The comparative receptor-ligand interaction analysis between hGPR17 and hP2Y1 receptor exposed the distinct binding sites in terms of geometrical properties. Further, the molecular docking of T0510.3657 with the hP2Y1 receptor have shown non-specific interaction. The experimental validation also revealed that Gi‐coupled activation of GPR17 by specific ligands leads to the adenylyl cyclase inhibition, while there is no inhibition upon hP2Y1 activation. Overall, the above findings suggest that T0510.3657-GPR17 binding specificity could be further explored for the treatment of numerous neuronal diseases. Communicated by Ramaswamy H. Sarma</p

Time window selection.

The three subplots represent the precision values for different time windows based on 21 start frames (x axis) and 12 window lengths (7 frames to 29 frames) for phases 1, 2, and 3 (from top to bottom) respectively, and the black bash line in each subplot indicates a precision value of 0.55.</p

iPS progenitor cells vs. MEFs and feature correlation.

(a) shows the examples of iPS progenitor cell images (blue circles) and normal MEFs images (yellow boxes) taken from phase 1, 2 and 3 of field 2 (Left, middle and right). Nucleus and cytoplasm of the enlarged progenitor cells and normal MEFs are colored in light blue and green respectively. (b) shows the Pearson coefficients between remaining types of features in three phases after the first step of feature selection. Note in this figure ellipsoid-prolate is denoted as E-prolate, intensity-StdDev as I-stdDev, intensity-min as I-Min, intensity-max as I-Max, nucleus-cytoplasm volume ratio as Ratio, ellipsoid-oblate as E-oblate.</p

Feature ranking and selection.

This figure shows how the precision values change with the deleted feature in a recursive fashion. Least important features are removed earlier.</p

Flow chart of the machine learning based approach for iPS progenitor cell identification.

In time-lapse imaging, we record the reprogramming process periodically among 54 fields after 48h of viral infection. For retrospective tracking, the figure only shows the reprogramming lineage images of the first frame of all eight phases. Only datasets from phase 1, 2 and 3 are used for model training and testing.</p

Model comparison for different missing frame number and imputation methods.

(a) shows the average precision over six time periods (TP1 to TP6) for each missing frame number and imputation method set_KNN (colored as blue), set_mean (colored as red), set_mean_mod (colored as green) and all three imputation methods (colored as gray). (b) shows the standard deviation, as a function of missing frame number, of imputation method set_KNN (colored as blue), set_mean (colored as red), set_mean_mod (colored as green) and all three imputation methods (colored as gray).</p

Model validation.

In all sub-figures, X axis indicates the start frame of the best time windows and the corresponding window length (13 frames) is indicated in the inlet. (a) 5-fold cross-validation precisions over 10 runs. (b) the standard deviation of the average precision of the neighborhood time windows in Fig 6D. (c) the standard deviation of the average precision of the distant windows in Fig 6E. (d) the average precision of seven neighborhood time windows calculated over 10 holdout validation runs. (e) the average precision over 10 independent tests for six best time windows on their corresponding distant windows.</p

Deep Learning-Based Bioactive Therapeutic Peptide Generation and Screening

Many bioactive peptides demonstrated therapeutic effects over complicated diseases, such as antiviral, antibacterial, anticancer, etc. It is possible to generate a large number of potentially bioactive peptides using deep learning in a manner analogous to the generation of de novo chemical compounds using the acquired bioactive peptides as a training set. Such generative techniques would be significant for drug development since peptides are much easier and cheaper to synthesize than compounds. Despite the limited availability of deep learning-based peptide-generating models, we have built an LSTM model (called LSTM_Pep) to generate de novo peptides and fine-tuned the model to generate de novo peptides with specific prospective therapeutic benefits. Remarkably, the Antimicrobial Peptide Database has been effectively utilized to generate various kinds of potential active de novo peptides. We proposed a pipeline for screening those generated peptides for a given target and used the main protease of SARS-COV-2 as a proof-of-concept. Moreover, we have developed a deep learning-based protein–peptide prediction model (DeepPep) for rapid screening of the generated peptides for the given targets. Together with the generating model, we have demonstrated that iteratively fine-tuning training, generating, and screening peptides for higher-predicted binding affinity peptides can be achieved. Our work sheds light on developing deep learning-based methods and pipelines to effectively generate and obtain bioactive peptides with a specific therapeutic effect and showcases how artificial intelligence can help discover de novo bioactive peptides that can bind to a particular target