Pr[m]: An Algorithm for Protein Motif Discovery

Motifs are the evolutionarily conserved patterns which are reported to serve the crucial structural and functional role. Identification of motif patterns in a set of protein sequences has been a prime concern for researchers in computational biology. The discovery of such a protein motif using existing algorithms is purely based on the parameters derived from sequence composition and length. However, the discovery of variable length motif remains a challenging task, as it is not possible to determine the length of a motif in advance. In current work, a k-mer based motif discovery approach called Pr[m], is proposed for the detection of the statistically significant un-gapped motif patterns, with or without wildcard characters. In order to analyze the performance of the proposed approach, a comparative study was performed with MEME and GLAM2, which are two widely used non-discriminative methods for motif discovery. A set of 7,500 test dataset were used to compare the performance of the proposed tool and the ones mentioned above. Pr[m] outperformed the existing methods in terms of predictive quality and performance. The proposed approach is hosted at https://bioserver.iiita.ac.in/Pr[m].

DeepOlf: Deep Neural Network Based Architecture for Predicting Odorants and Their Interacting Olfactory Receptors

Olfaction transduction mechanism is triggered by the binding of odorants to the specific olfactory receptors (OR's) present in the nasal cavity. Different odorants stimulate different OR's due to the difference in shape, physical and chemical properties. In this paper, a deep neural network architecture DeepOlf, based on molecular features and fingerprints of odorants and ORs, to predict whether a chemical compound is a potential odorant or not along with its interacting OR is proposed. Odorant identification and Odorant-OR interaction were modeled as a binary classification through multiple classifiers. The evaluation of these classifier's performance showed that the deep-neural network framework not only fits data with better accuracy in comparison to other classical methods (SVM, RF, k-NN) but also able to predict odorant-OR interactions more accurately. To our knowledge, this study is the first realization of deep learning ideas for the problem of odorant and interacting OR prediction. The accuracy of DeepOlf was found to be 94.83 and 99.92 percent for the prediction of odorants and Odorant- OR interactions respectively. Comparison of DeepOlf prediction with the existing SVM based prediction server, ODORactor, showed that better performance can be achieved with the proposed deep learning approach. The DeepOlf tool can be accessed at https://bioserver.iiita.ac.in/deepolf/.

Author Correction: Identification of novel dysregulated key genes in Breast cancer through high throughput ChIP-Seq data analysis

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

In silico identification of therapeutic compounds against microRNA targets in drug-resistant pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a major health issue that has been eluding efforts to identify viable therapeutic treatment options. Besides having the lowest survival rate among all types of cancer, almost all conventional methods of treatment are futile against this condition, leaving patients to succumb to this ailment faster than ever. As it is increasingly becoming difficult to come up with new compounds for the treatment of various diseases, alternative solutions are required for tackling these problems. In this study, publically available miRNA and gene expression data were used to identify common elements that were present in gemcitabine-resistant PDAC cell lines. By selecting overexpressed genes involved in pancreatic cancer and cancer pathways in general, potential drug candidates for the treatment of PDAC were identified. In this study, 21 differentially expressed miRNAs were identified from PANC-1 cell line treated with gemcitabine. Pathway analysis revealed that MET and PPARG were overexpressed in cancer-related pathways, including pancreatic cancer, and could be targeted for PDAC treatment. Using CMap, fisetin was identified a likely candidate drug for the treatment of PDAC. Docking studies indicated that fisetin was bound to c-Met and PPARG with an XP G score of -12.819 and -7.021 kcal/mol, respectively. As miRNAs have increasingly been shown to part take in important cancer-related processes and pathways, researching drug development methods based on miRNA targets could be beneficial for pharmaceutical industries. Communicated by Ramaswamy H. Sarma.

DeEPn: a deep neural network based tool for enzyme functional annotation

With the advancement of high throughput techniques, the discovery rate of enzyme sequences has increased significantly in the recent past. All of these raw sequences are required to be precisely mapped to their respective functional attributes, which helps in deciphering their biological role. In the recent past, various prediction models have been proposed to predict the enzyme functional class; however, all of these models were able to quantify at most six functional enzyme classes (EC1 to EC6) out of existing seven functional classes, making these approaches inappropriate for handling enzymes corresponding to the seventh functional class (EC7). In this study, a Deep Neural Network-based approach, DeEPn, has been proposed, which can quantify enzymes corresponding to all seven functional classes with high precision and accuracy. The proposed model was compared with two recently developed tools, ECPred and SVM-Prot. The result demonstrated that DeEPn outperformed ECPred and SVM-Prot in terms of predictive quality. The DeEPn tool has been hosted as a web-based tool at https://bioserver.iiita.ac.in/DeEPn/.Communicated by Ramaswamy H. Sarma.

Comparative modeling and structure based drug repurposing of PAX2 transcription factor for targeting acquired chemoresistance in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is a pancreatic malignancy suffering from poor prognosis; the worst among all types of cancer. Chemotherapy, which is the standard regime for treatment in most cases, is often rendered useless as drug resistance quickly sets in after prolonged exposure to the drug. The implication of PAX2 transcription factor in regulating several ATP-binding cassette (ABC) transporter proteins that are responsible for the acquisition of drug resistance in PDAC makes it a potential target for treatment purposes. In this study, the 3D structure of PAX2 protein was modeled, and the response of key amino acids to perturbation was identified. Subsequently, kappadione, a vitamin K derivative, was found to bind efficiently to PAX2 with a binding energy of -9.819 kcal/mol. The efficacy of mechanism and mode of binding was studied by docking the protein with DNA in the presence and absence of the drug. The presence of kappadione disrupted DNA binding with key effector resides, preventing the DNA from coming into contact with the binding region essential for protein translation. By occupying the DNA binding region and replacing it with a ligand, the mechanism by which DNA interacts with PAX2 could be manipulated. Inhibition of PAX2-DNA binding using kappadione and other small molecules can prove to be beneficial for combating chemoresistance in PDAC, as proposed through in silico approaches.Communicated by Ramaswamy H. Sarma.

Unravelling the role of long non-coding RNA - LINC01087 in breast cancer

Apoptosis is a 'programmed fate' of all cells participating in diverse physiological and pathological conditions. The role of critical regulators and their involvement in this complex multi-stage process of apoptosis weaved around non-coding RNAs (ncRNAs) is poorly deciphered in breast carcinoma (BC). Aberrant expression patterns of the ncRNAs and their interacting partners, either ncRNAs or coding RNAs or proteins at any point along these pathways, may lead to the malignant transformation of the affected cells, tumour metastasis and resistance to anticancer drugs. Longest non-coding type of ncRNAs (lncRNAs) have been considered as critical factors for the development and progression of breast cancer. The aim of our study was to identify set of novel lncRNAs interacting with microRNAs (miRNAs) or proteins that were significantly dysregulated in breast cancer using RNA-Sequencing (RNA-Seq) technique in different samples acting as oncogenic drivers contributing to cancerous phenotype involved in post-transcriptional processing of RNAs. Four lncRNAs; LINC01087, lnc-CLSTN2-1:1, lnc-c7orf65-3:3 and LINC01559:2 were selected for further analysis. Gene expression analysis of over-expressed LINC01087 in vitro reduced both cell viability and apoptosis. We integrated miRNA and mRNA (hsa-miR-548 and AKT1) expression profiles with curated regulations with lncRNA (LINC01087) which has not been previously associated with any breast cancer type, using different computational tools. The network (lncRNA→ miRNA→ mRNA) is promising for the identification of carcinoma associated genes and apoptosis signaling path highlighting the potential roles of LINC01087, hsa-miR548n, AKT1 gene which may play crucial role in proliferation.

Understanding the Mechanism of Cell Death in Gemcitabine Resistant Pancreatic Ductal Adenocarcinoma: A Systems Biology Approach

BACKGROUND

Gemcitabine is the standard chemotherapeutic drug administered in advanced Pancreatic Ductal Adenocarcinoma (PDAC). However, due to drug resistance in PDAC patients, this treatment has become less effective. Over the years, clinical trials for the quest of finding novel compounds that can be used in combination with gemcitabine have met very little success.

OBJECTIVE

To predict the driving factors behind pancreatic ductal adenocarcinoma, and to understand the effect of these components in the progression of the disease and their contribution to cell growth and proliferation.

METHODS

With the help of systems biology approaches and using gene expression data, which is generally found in abundance, dysregulated elements in key signalling pathways were predicted. Prominent dysregulated elements were integrated into a model to simulate and study the effect of gemcitabine-induced hypoxia.

RESULTS

In this study, several transcription factors in the form of key drivers of cancer-related genes were predicted with the help of CARNIVAL, and the effect of gemcitabine-induced hypoxia on the apoptosis pathway was shown to have an effect on the downstream elements of two primary pathway models; EGF/VEGF and TNF signalling pathway.

CONCLUSION

It was observed that EGF/VEGF signalling pathway played a major role in inducing drug resistance through cell growth, proliferation, and avoiding cell death. Targeting the major upstream components of this pathway could potentially lead to successful treatment.

An integrated epigenome and transcriptome analysis identifies PAX2 as a master regulator of drug resistance in high grade pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is notoriously difficult to treat due to its aggressive, ever resilient nature. A major drawback lies in its tumor grade; a phenomenon observed across various carcinomas, where highly differentiated and undifferentiated tumor grades, termed as low and high grade respectively, are found in the same tumor. One eminent problem due to such heterogeneity is drug resistance in PDAC. This has been implicated to ABC transporter family of proteins that are upregulated in PDAC patients. However, the regulation of these transporters with respect to tumor grade in PDAC is not well understood. To combat these issues, a study was designed to identify novel genes that might regulate drug resistance phenotype and be used as targets. By integrating epigenome with transcriptome data, several genes were identified based around high grade PDAC. Further analysis indicated oncogenic PAX2 transcription factor as a novel regulator of drug resistance in high grade PDAC cell lines. It was observed that silencing of PAX2 resulted in increased susceptibility of high grade PDAC cells to various chemotherapeutic drugs. Mechanistically, the study showed that PAX2 protein can bind and alter transcriptionally; expression of many ABC transporter genes in high grade PDAC cell lines. Overall, the study indicated that PAX2 significantly upregulated ABC family of genes resulting in drug resistance and poor survival in PDAC.

Sense of Smell: Structural, Functional, Mechanistic Advancements and Challenges in Human Olfactory Research

Olfaction, the sense of smell detects and discriminate odors as well as social cues which influence our innate responses. The olfactory system in human beings is found to be weak as compared to other animals; however, it seems to be very precise. It can detect and discriminate millions of chemical moieties (odorants) even in minuscule quantities. The process initiates with the binding of odorants to specialized olfactory receptors, encoded by a large family of Olfactory Receptor (OR) genes belonging to the G-protein-coupled receptor superfamily. Stimulation of ORs converts the chemical information encoded in the odorants, into respective neuronal action-potentials which causes depolarization of olfactory sensory neurons. The olfactory bulb relays this signal to different parts of the brain for processing. Odors are encrypted using a combinatorial approach to detect a variety of chemicals and encode their unique identity. The discovery of functional OR genes and proteins provided an important information to decipher the genomic, structural and functional basis of olfaction. ORs constitute 17 gene families, out of which 4 families were reported to contain more than hundred members each. The olfactory machinery is not limited to GPCRs; a number of non- GPCRs is also employed to detect chemosensory stimuli. The article provides detailed information about such olfaction machinery, structures, transduction mechanism, theories of odor perception, and challenges in the olfaction research. It covers the structural, functional and computational studies carried out in the olfaction research in the recent past.

A systematic assessment of statistics, risk factors, and underlying features involved in pancreatic cancer

Pancreatic cancer remains the fourth leading cause of cancer-related death in the world, and will continue to become the number two cause of cancer-related death unless a remarkable breakthrough is achieved. With a slim chance of early diagnosis, surgery can only provide a median survival of 17-23 months. The presence of a dense stroma makes this cancer resilient to chemotherapy, with very few potent inhibitors like nab paclitaxelin available that can work in combination with chemotherapeutic agents. Survival rates, on the one hand, lie at 8.5%. Variation in types of pancreatic cancer, on the other hand, makes it notoriously difficult to come up with a practical solution for the treatment of this disease. A deeper understanding of the root cause would be beneficial for diagnosis. Advancement in the field of genomics has made the identification of novel biomarkers relatively easier. By coupling this factor with the production of suitable inhibitors, testing in large numbers can be made possible with the help of cell lines. With the combined efforts of biological knowledge and modern technology, the cure for pancreatic cancer could be at hand.

Identification of novel dysregulated key genes in Breast cancer through high throughput ChIP-Seq data analysis

Breast cancer is the most common cancer in women both in the developed and less developed countries, and it imposes a considerable threat to human health. Therefore, in order to develop effective targeted therapies against Breast cancer, a deep understanding of its underlying molecular mechanisms is required. The application of deep transcriptional sequencing has been found to be reported to provide an efficient genomic assay to delve into the insights of the diseases and may prove to be useful in the study of Breast cancer. In this study, ChIP-Seq data for normal samples and Breast cancer were compared, and differential peaks identified, based upon fold enrichment (with P-values obtained via t-tests). The Protein-protein interaction (PPI) network analysis was carried out, following which the highly connected genes were screened and studied, and the most promising ones were selected. Biological pathway involved in the process were then identified. Our findings regarding potential Breast cancer-related genes enhances the understanding of the disease and provides prognostic information in addition to standard tumor prognostic factors for future research.

Structural insights into conformational stability of both wild-type and mutant EZH2 receptor

Polycomb group (PcG) proteins have been observed to maintain the pattern of histone by methylation of the histone tail responsible for the gene expression in various cellular processes, of which enhancer of zeste homolog 2 (EZH2) acts as tumor suppressor. Overexpression of EZH2 results in hyper activation found in a variety of cancer. Point mutation on two important residues were induced and the results were compared between the wild type and mutant EZH2. The mutation of Y641 and A677 present in the active region of the protein alters the interaction of the top ranked compound with the newly modeled binding groove of the SET domain, giving a GLIDE score of -12.26 kcal/mol, better than that of the wild type at -11.664 kcal/mol. In depth analysis were carried out for understanding the underlying molecular mechanism using techniques viz. molecular dynamics, principal component analysis, residue interaction network and free energy landscape analysis, which showed that the mutated residues changed the overall conformation of the system along with the residue-residue interaction network. The insight from this study could be of great relevance while designing new compounds for EZH2 enzyme inhibition and the effect of mutation on the overall binding mechanism of the system.