NPalmitoylDeep-PseAAC: A Predictor for N-Palmitoylation sites in Proteins using Deep Representations of Proteins and PseAAC via modified 5-steps rule

2020 ◽  
Vol 15 ◽  
Author(s):  
Sheraz Naseer ◽  
Waqar Hussain ◽  
Yaser Daanial Khan ◽  
Nouman Rasool
Keyword(s):  
Neural Networks ◽  
Prediction Model ◽  
Deep Neural Networks ◽  
Ex Vivo ◽  
Feature Representation ◽  
Post Translational Modification ◽  
Lipid Modifications ◽  
Gated Recurrent Unit

Background: Among all the major Post-translational modification, lipid modifications possess special significance due to their widespread functional importance in eukaryotic cells. There exist multiple types of lipid modifications and Palmitoylation, among them, is one of the broader types of modification, having three different types. The N-Palmitoylation is carried out by attachment of palmitic acid to an N-terminal cysteine. Due to the association of N-Palmitoylation with various biological functions and diseases such as Alzheimer’s and other neurodegenerative diseases, carrying out important processes in the life cycle of various pathogens, its identification is very important. Objective: The in vitro, ex vivo and in vivo identification of Palmitoylation is laborious, time-taking and costly. There is a dire need of an efficient and accurate computational model to help researchers and biologists identifying these sites, in an easy manner. Herein, we propose a novel prediction model for identification of N-Palmitoylation sites in proteins. Method: Proposed prediction model is developed by combining the Chou’s Pseudo Amino Acid Composition (PseAAC) with deep neural networks. We used well-known deep neural networks (DNNs) for both the tasks of learning a feature representation of peptide sequences and developing prediction model to perform classification. Results: Among different DNNs, Gated Recurrent Unit (GRU) based RNN model showed highest scores in terms of accuracy, and all other computed measures, and outperforms all the previously reported predictors. Conclusion: The proposed GRU based RNN model can help identifying N-Palmitoylation in a very efficient and accurate manner which can help scientists understand the mechanism of this modification in proteins.

scholarly journals Computational identification of 4-carboxyglutamate sites to supplement physiological studies using deep learning

2022 ◽  
Vol 12 (1) ◽  
Author(s):  
Sheraz Naseer ◽  
Rao Faizan Ali ◽  
Suliman Mohamed Fati ◽  
Amgad Muneer
Keyword(s):  
Neural Networks ◽  
Amino Acid ◽  
Glutamic Acid ◽  
Deep Neural Networks ◽  
Ex Vivo ◽  
Protein Biosynthesis ◽  
Feature Representation ◽  
Post Translational Modification

AbstractIn biological systems, Glutamic acid is a crucial amino acid which is used in protein biosynthesis. Carboxylation of glutamic acid is a significant post-translational modification which plays important role in blood coagulation by activating prothrombin to thrombin. Contrariwise, 4-carboxy-glutamate is also found to be involved in diseases including plaque atherosclerosis, osteoporosis, mineralized heart valves, bone resorption and serves as biomarker for onset of these diseases. Owing to the pathophysiological significance of 4-carboxyglutamate, its identification is important to better understand pathophysiological systems. The wet lab identification of prospective 4-carboxyglutamate sites is costly, laborious and time consuming due to inherent difficulties of in-vivo, ex-vivo and in vitro experiments. To supplement these experiments, we proposed, implemented, and evaluated a different approach to develop 4-carboxyglutamate site predictors using pseudo amino acid compositions (PseAAC) and deep neural networks (DNNs). Our approach does not require any feature extraction and employs deep neural networks to learn feature representation of peptide sequences and performing classification thereof. Proposed approach is validated using standard performance evaluation metrics. Among different deep neural networks, convolutional neural network-based predictor achieved best scores on independent dataset with accuracy of 94.7%, AuC score of 0.91 and F1-score of 0.874 which shows the promise of proposed approach. The iCarboxE-Deep server is deployed at https://share.streamlit.io/sheraz-n/carboxyglutamate/app.py.

scholarly journals MOLI: multi-omics late integration with deep neural networks for drug response prediction

2019 ◽  
Vol 35 (14) ◽  
pp. i501-i509 ◽  
Author(s):  
Hossein Sharifi-Noghabi ◽  
Olga Zolotareva ◽  
Colin C Collins ◽  
Martin Ester
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Prediction Accuracy ◽  
Drug Response ◽  
Deep Neural Networks ◽  
Response Prediction ◽  
Supplementary Information ◽  
Precision Oncology

Abstract Motivation Historically, gene expression has been shown to be the most informative data for drug response prediction. Recent evidence suggests that integrating additional omics can improve the prediction accuracy which raises the question of how to integrate the additional omics. Regardless of the integration strategy, clinical utility and translatability are crucial. Thus, we reasoned a multi-omics approach combined with clinical datasets would improve drug response prediction and clinical relevance. Results We propose MOLI, a multi-omics late integration method based on deep neural networks. MOLI takes somatic mutation, copy number aberration and gene expression data as input, and integrates them for drug response prediction. MOLI uses type-specific encoding sub-networks to learn features for each omics type, concatenates them into one representation and optimizes this representation via a combined cost function consisting of a triplet loss and a binary cross-entropy loss. The former makes the representations of responder samples more similar to each other and different from the non-responders, and the latter makes this representation predictive of the response values. We validate MOLI on in vitro and in vivo datasets for five chemotherapy agents and two targeted therapeutics. Compared to state-of-the-art single-omics and early integration multi-omics methods, MOLI achieves higher prediction accuracy in external validations. Moreover, a significant improvement in MOLI’s performance is observed for targeted drugs when training on a pan-drug input, i.e. using all the drugs with the same target compared to training only on drug-specific inputs. MOLI’s high predictive power suggests it may have utility in precision oncology. Availability and implementation https://github.com/hosseinshn/MOLI. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Deciphering the rules of mRNA structure differentiation in Saccharomyces cerevisiae in vivo and in vitro with deep neural networks

RNA Biology ◽  
2019 ◽  
Vol 16 (8) ◽  
pp. 1044-1054 ◽  
Author(s):  
Haopeng Yu ◽  
Wenjing Meng ◽  
Yuanhui Mao ◽  
Yi Zhang ◽  
Qing Sun ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Mrna Structure

scholarly journals iAmideV-Deep: Valine Amidation Site Prediction in Proteins Using Deep Learning and Pseudo Amino Acid Compositions

Symmetry ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 560
Author(s):  
Sheraz Naseer ◽  
Rao Faizan Ali ◽  
Amgad Muneer ◽  
Suliman Mohamed Fati
Keyword(s):  
Amino Acid ◽  
Computational Models ◽  
Ex Vivo ◽  
Biologically Active ◽  
Peptide Sequence ◽  
Post Translational Modification ◽  
Amino Acid Compositions ◽  
Efficient Alternative

Amidation is an important post translational modification where a peptide ends with an amide group (–NH2) rather than carboxyl group (–COOH). These amidated peptides are less sensitive to proteolytic degradation with extended half-life in the bloodstream. Amides are used in different industries like pharmaceuticals, natural products, and biologically active compounds. The in-vivo, ex-vivo, and in-vitro identification of amidation sites is a costly and time-consuming but important task to study the physiochemical properties of amidated peptides. A less costly and efficient alternative is to supplement wet lab experiments with accurate computational models. Hence, an urgent need exists for efficient and accurate computational models to easily identify amidated sites in peptides. In this study, we present a new predictor, based on deep neural networks (DNN) and Pseudo Amino Acid Compositions (PseAAC), to learn efficient, task-specific, and effective representations for valine amidation site identification. Well-known DNN architectures are used in this contribution to learn peptide sequence representations and classify peptide chains. Of all the different DNN based predictors developed in this study, Convolutional neural network-based model showed the best performance surpassing all other DNN based models and reported literature contributions. The proposed model will supplement in-vivo methods and help scientists to determine valine amidation very efficiently and accurately, which in turn will enhance understanding of the valine amidation in different biological processes.

scholarly journals MOLI: Multi-Omics Late Integration with deep neural networks for drug response prediction

2019 ◽  
Author(s):  
Hossein Sharifi-Noghabi ◽  
Olga Zolotareva ◽  
Colin C. Collins ◽  
Martin Ester
Keyword(s):  
Gene Expression ◽  
Neural Networks ◽  
Prediction Accuracy ◽  
Drug Response ◽  
Deep Neural Networks ◽  
Response Prediction ◽  
Precision Oncology ◽  
Early Integration

AbstractMotivationHistorically, gene expression has been shown to be the most informative data for drug response prediction. Recent evidence suggests that integrating additional omics can improve the prediction accuracy which raises the question of how to integrate the additional omics. Regardless of the integration strategy, clinical utility and translatability are crucial. Thus, we reasoned a multi-omics approach combined with clinical datasets would improve drug response prediction and clinical relevance.ResultsWe propose MOLI, a Multi-Omics Late Integration method based on deep neural networks. MOLI takes somatic mutation, copy number aberration, and gene expression data as input, and integrates them for drug response prediction. MOLI uses type-specific encoding subnetworks to learn features for each omics type, concatenates them into one representation and optimizes this representation via a combined cost function consisting of a triplet loss and a binary cross-entropy loss. The former makes the representations of responder samples more similar to each and different from the non-responders, and the latter makes this representation predictive of the response values. We validate MOLI on in vitro and in vivo datasets for five chemotherapy agents and two targeted therapeutics. Compared to state-of-the-art single-omics and early integration multi-omics methods, MOLI achieves higher prediction accuracy in external validations. Moreover, a significant improvement in MOLI’s performance is observed for targeted drugs when training on a pan-drug input, i.e. using all the drugs with the same target compared to training only on drug-specific inputs. MOLI’s high predictive power suggests it may have utility in precision oncology.Availability of the implemented codeshttps://github.com/hosseinshn/[email protected] and [email protected]

Sequence-based Identification of Arginine Amidation Sites in Proteins Using Deep Representations of Proteins and PseAAC

2021 ◽  
Vol 15 (8) ◽  
pp. 937-948
Author(s):  
Sheraz Naseer ◽  
Waqar Hussain ◽  
Yaser Daanial Khan ◽  
Nouman Rasool
Keyword(s):  
Ex Vivo ◽  
Amide Group ◽  
Feature Representation ◽  
Carboxyl Group ◽  
Pseudo Amino Acid Composition ◽  
Post Translational Modifications ◽  
Proposed Model ◽  
Small Change

Background: Among all the major post-translational modifications, amidation seems to be a small change, where a peptide ends with an amide group (-NH 2), not a carboxyl group (-COOH). Thus, to study their physicochemical properties, identification of the amidation mechanism is very important. However, the in vitro, ex vivo and in vivo identification can be laborious, time-taking and costly. There is a dire need for an efficient and accurate computational model to help researchers and biologists identifying these sites, in an easy manner. Objectives: Herein, we propose a novel predictor for the identification of arginine amide (R-Amide) sites in proteins, by integrating the Chou’s Pseudo Amino Acid Composition (PseAAC) with deep features. Methods: We use well-known DNNs for both the tasks of learning a feature representation of peptide sequences and performing classifications. Methods: We use well-known DNNs for both the tasks of learning a feature representation of peptide sequences and performing classifications. Results: Among different DNNs, CNN showed the highest scores in terms of accuracy, and all other computed measures outperformed all the previously reported predictors. Conclusions: Based on these results, it is concluded that the proposed model can help identify arginine amidation in a very efficient and accurate manner, which can help scientists understand the mechanism of this modification in proteins.

The Role of Polyphenols in the Modulation of Plasma Non-Enzymatic Antioxidant Capacity (NEAC)

2012 ◽  
Vol 82 (3) ◽  
pp. 228-232 ◽  
Author(s):  
Mauro Serafini ◽  
Giuseppa Morabito
Keyword(s):  
Antioxidant Capacity ◽  
Dietary Intervention ◽  
Ex Vivo ◽  
The Body ◽  
Dietary Polyphenols ◽  
Enzymatic Antioxidant ◽  
Endogenous Antioxidant

Dietary polyphenols have been shown to scavenge free radicals, modulating cellular redox transcription factors in different in vitro and ex vivo models. Dietary intervention studies have shown that consumption of plant foods modulates plasma Non-Enzymatic Antioxidant Capacity (NEAC), a biomarker of the endogenous antioxidant network, in human subjects. However, the identification of the molecules responsible for this effect are yet to be obtained and evidences of an antioxidant in vivo action of polyphenols are conflicting. There is a clear discrepancy between polyphenols (PP) concentration in body fluids and the extent of increase of plasma NEAC. The low degree of absorption and the extensive metabolism of PP within the body have raised questions about their contribution to the endogenous antioxidant network. This work will discuss the role of polyphenols from galenic preparation, food extracts, and selected dietary sources as modulators of plasma NEAC in humans.

Significance of Platelet β-Adrenoceptors for Platelet Responses In Vivo and In Vitro

1992 ◽  
Vol 68 (06) ◽  
pp. 687-693 ◽  
Author(s):  
P T Larsson ◽  
N H Wallén ◽  
A Martinsson ◽  
N Egberg ◽  
P Hjemdahl
Keyword(s):  
Ex Vivo ◽  
High Dose ◽  
Platelet Aggregability ◽  
Adrenoceptor Agonist ◽  
Dose Infusion ◽  
Von Willebrand ◽  
Willebrand Factor ◽  
Factor Antigen

SummaryThe significance of platelet β-adrenoceptors for platelet responses to adrenergic stimuli in vivo and in vitro was studied in healthy volunteers. Low dose infusion of the β-adrenoceptor agonist isoprenaline decreased platelet aggregability in vivo as measured by ex vivo filtragometry. Infusion of adrenaline, a mixed α- and β-adrenoceptor agonist, increased platelet aggregability in vivo markedly, as measured by ex vivo filtragometry and plasma β-thromboglobulin levels. Adrenaline levels were 3–4 nM in venous plasma during infusion. Both adrenaline and high dose isoprenaline elevated plasma von Willebrand factor antigen levels β-Blockade by propranolol did not alter our measures of platelet aggregability at rest or during adrenaline infusions, but inhibited adrenaline-induced increases in vWf:ag. In a model using filtragometry to assess platelet aggregability in whole blood in vitro, propranolol enhanced the proaggregatory actions of 5 nM, but not of 10 nM adrenaline. The present data suggest that β-adrenoceptor stimulation can inhibit platelet function in vivo but that effects of adrenaline at high physiological concentrations are dominated by an α-adrenoceptor mediated proaggregatory action.

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