Recent Advances in the System Biology-based Target Identification and Drug Discovery

2018 ◽  
Vol 18 (20) ◽  
pp. 1737-1744 ◽  
Author(s):  
Brijesh Singh Yadav ◽  
Vijay Tripathi
Keyword(s):  
System Biology ◽  
Large Scale ◽  
Computational Models ◽  
Target Identification ◽  
Complex Data ◽  
Data Systems ◽  
Network Analyses ◽  
Molecular Determinant ◽  
Ligand Interactions ◽  
And Function

The enormous quantity of publicly available active chemical ligand and biological receptor data knowledge allows scientists to retreat several open questions by the analysis and systematic integration of these complex unique data. Systems biology plays a crucial role through the constructive alignment of bio-physiochemical monitoring of gene, protein along with metabolites from the complex data. Further, it integrates information within the data and responses (metabolic and signaling pathway) which lead to the formulation of computational models for the elucidation of structure and function of the molecular determinant. The system biology methods utilize big complex high throughput data for the identification of the whole drug target and for the mechanism of action to lead compound characterization. Nowadays, the system biology is one of the most popular approaches to characterize proteinligand interaction on a large scale and is vital to address a complex mode of the drug action to clinical indications. The network of protein-ligand interactions also reveals the correlation between molecular functions of the cell with their physiological processes which help to design safe and effective ligands for drug development. Here, we review recent attempts to apply system biology-based approaches with large-scale network analyses to predict novel interactions of ligand and targets. We also deliver an essential step involved in the discovery and development of such multi‐target drugs by identifying the group of proteins targeted by a particular ligand, leading to innovation in therapeutic research.

scholarly journals Data Management and Modeling in Plant Biology

2021 ◽  
Vol 12 ◽  
Author(s):  
Maria Krantz ◽  
David Zimmer ◽  
Stephan O. Adler ◽  
Anastasia Kitashova ◽  
Edda Klipp ◽  
...  
Keyword(s):  
Data Storage ◽  
Large Scale ◽  
Computational Models ◽  
Quantitative Model ◽  
Plant Biology ◽  
Complex Data ◽  
Computational Statistics ◽  
Plant Environment ◽  
Experimental Findings ◽  
Statistical Trends

The study of plant-environment interactions is a multidisciplinary research field. With the emergence of quantitative large-scale and high-throughput techniques, amount and dimensionality of experimental data have strongly increased. Appropriate strategies for data storage, management, and evaluation are needed to make efficient use of experimental findings. Computational approaches of data mining are essential for deriving statistical trends and signatures contained in data matrices. Although, current biology is challenged by high data dimensionality in general, this is particularly true for plant biology. Plants as sessile organisms have to cope with environmental fluctuations. This typically results in strong dynamics of metabolite and protein concentrations which are often challenging to quantify. Summarizing experimental output results in complex data arrays, which need computational statistics and numerical methods for building quantitative models. Experimental findings need to be combined by computational models to gain a mechanistic understanding of plant metabolism. For this, bioinformatics and mathematics need to be combined with experimental setups in physiology, biochemistry, and molecular biology. This review presents and discusses concepts at the interface of experiment and computation, which are likely to shape current and future plant biology. Finally, this interface is discussed with regard to its capabilities and limitations to develop a quantitative model of plant-environment interactions.

Plant hormones, plant growth regulators

2014 ◽  
Vol 155 (26) ◽  
pp. 1011-1018 ◽  
Author(s):  
György Végvári ◽  
Edina Vidéki
Keyword(s):  
Nervous System ◽  
Growth And Development ◽  
Large Scale ◽  
Essential Role ◽  
Higher Plants ◽  
Structure And Function ◽  
Wide Sense ◽  
Large Scale Integration ◽  
Scale Integration ◽  
And Function

Plants seem to be rather defenceless, they are unable to do motion, have no nervous system or immune system unlike animals. Besides this, plants do have hormones, though these substances are produced not in glands. In view of their complexity they lagged behind animals, however, plant organisms show large scale integration in their structure and function. In higher plants, such as in animals, the intercellular communication is fulfilled through chemical messengers. These specific compounds in plants are called phytohormones, or in a wide sense, bioregulators. Even a small quantity of these endogenous organic compounds are able to regulate the operation, growth and development of higher plants, and keep the connection between cells, tissues and synergy beween organs. Since they do not have nervous and immume systems, phytohormones play essential role in plants’ life. Orv. Hetil., 2014, 155(26), 1011–1018.

DeepSSPred: A Deep Learning Based Sulfenylation site predictor via a novel n-segmented optimize federated feature encoder

2020 ◽  
Vol 27 ◽  
Author(s):  
Zaheer Ullah Khan ◽  
Dechang Pi
Keyword(s):  
Large Scale ◽  
Computational Models ◽  
Research Work ◽  
Training Data ◽  
Training Dataset ◽  
Validation Dataset ◽  
Cytokine Signaling ◽  
Minority Class ◽  
Independent Dataset ◽  
Feature Encoding

Background: S-sulfenylation (S-sulphenylation, or sulfenic acid) proteins, are special kinds of post-translation modification, which plays an important role in various physiological and pathological processes such as cytokine signaling, transcriptional regulation, and apoptosis. Despite these aforementioned significances, and by complementing existing wet methods, several computational models have been developed for sulfenylation cysteine sites prediction. However, the performance of these models was not satisfactory due to inefficient feature schemes, severe imbalance issues, and lack of an intelligent learning engine. Objective: In this study, our motivation is to establish a strong and novel computational predictor for discrimination of sulfenylation and non-sulfenylation sites. Methods: In this study, we report an innovative bioinformatics feature encoding tool, named DeepSSPred, in which, resulting encoded features is obtained via n-segmented hybrid feature, and then the resampling technique called synthetic minority oversampling was employed to cope with the severe imbalance issue between SC-sites (minority class) and non-SC sites (majority class). State of the art 2DConvolutional Neural Network was employed over rigorous 10-fold jackknife cross-validation technique for model validation and authentication. Results: Following the proposed framework, with a strong discrete presentation of feature space, machine learning engine, and unbiased presentation of the underline training data yielded into an excellent model that outperforms with all existing established studies. The proposed approach is 6% higher in terms of MCC from the first best. On an independent dataset, the existing first best study failed to provide sufficient details. The model obtained an increase of 7.5% in accuracy, 1.22% in Sn, 12.91% in Sp and 13.12% in MCC on the training data and12.13% of ACC, 27.25% in Sn, 2.25% in Sp, and 30.37% in MCC on an independent dataset in comparison with 2nd best method. These empirical analyses show the superlative performance of the proposed model over both training and Independent dataset in comparison with existing literature studies. Conclusion : In this research, we have developed a novel sequence-based automated predictor for SC-sites, called DeepSSPred. The empirical simulations outcomes with a training dataset and independent validation dataset have revealed the efficacy of the proposed theoretical model. The good performance of DeepSSPred is due to several reasons, such as novel discriminative feature encoding schemes, SMOTE technique, and careful construction of the prediction model through the tuned 2D-CNN classifier. We believe that our research work will provide a potential insight into a further prediction of S-sulfenylation characteristics and functionalities. Thus, we hope that our developed predictor will significantly helpful for large scale discrimination of unknown SC-sites in particular and designing new pharmaceutical drugs in general.

scholarly journals A Novel Method to Predict Drug-Target Interactions Based on Large-Scale Graph Representation Learning

Cancers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 2111
Author(s):  
Bo-Wei Zhao ◽  
Zhu-Hong You ◽  
Lun Hu ◽  
Zhen-Hao Guo ◽  
Lei Wang ◽  
...  
Keyword(s):  
Drug Target ◽  
Large Scale ◽  
Computational Models ◽  
Structural Information ◽  
Characteristic Curve ◽  
Representation Learning ◽  
Graph Representation ◽  
Convolutional Network ◽  
Novel Method

Identification of drug-target interactions (DTIs) is a significant step in the drug discovery or repositioning process. Compared with the time-consuming and labor-intensive in vivo experimental methods, the computational models can provide high-quality DTI candidates in an instant. In this study, we propose a novel method called LGDTI to predict DTIs based on large-scale graph representation learning. LGDTI can capture the local and global structural information of the graph. Specifically, the first-order neighbor information of nodes can be aggregated by the graph convolutional network (GCN); on the other hand, the high-order neighbor information of nodes can be learned by the graph embedding method called DeepWalk. Finally, the two kinds of feature are fed into the random forest classifier to train and predict potential DTIs. The results show that our method obtained area under the receiver operating characteristic curve (AUROC) of 0.9455 and area under the precision-recall curve (AUPR) of 0.9491 under 5-fold cross-validation. Moreover, we compare the presented method with some existing state-of-the-art methods. These results imply that LGDTI can efficiently and robustly capture undiscovered DTIs. Moreover, the proposed model is expected to bring new inspiration and provide novel perspectives to relevant researchers.

scholarly journals Advancing Drug Discovery for Neurological Disorders Using iPSC-Derived Neural Organoids

2021 ◽  
Vol 22 (5) ◽  
pp. 2659
Author(s):  
Gianluca Costamagna ◽  
Giacomo Pietro Comi ◽  
Stefania Corti
Keyword(s):  
Drug Discovery ◽  
Drug Screening ◽  
Neural Development ◽  
Neurological Disorders ◽  
Large Scale ◽  
Three Dimensional ◽  
Induced Pluripotent Stem Cell ◽  
Cellular Level ◽  
Complex Data ◽  
Complex Data Sets

In the last decade, different research groups in the academic setting have developed induced pluripotent stem cell-based protocols to generate three-dimensional, multicellular, neural organoids. Their use to model brain biology, early neural development, and human diseases has provided new insights into the pathophysiology of neuropsychiatric and neurological disorders, including microcephaly, autism, Parkinson’s disease, and Alzheimer’s disease. However, the adoption of organoid technology for large-scale drug screening in the industry has been hampered by challenges with reproducibility, scalability, and translatability to human disease. Potential technical solutions to expand their use in drug discovery pipelines include Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) to create isogenic models, single-cell RNA sequencing to characterize the model at a cellular level, and machine learning to analyze complex data sets. In addition, high-content imaging, automated liquid handling, and standardized assays represent other valuable tools toward this goal. Though several open issues still hamper the full implementation of the organoid technology outside academia, rapid progress in this field will help to prompt its translation toward large-scale drug screening for neurological disorders.

scholarly journals Modeled 3D-Structures of Proteobacterial Transglycosylases from Glycoside Hydrolase Family 17 Give Insight in Ligand Interactions Explaining Differences in Transglycosylation Products

2021 ◽  
Vol 11 (9) ◽  
pp. 4048
Author(s):  
Javier A. Linares-Pastén ◽  
Lilja Björk Jonsdottir ◽  
Gudmundur O. Hreggvidsson ◽  
Olafur H. Fridjonsson ◽  
Hildegard Watzlawick ◽  
...  
Keyword(s):  
Glycoside Hydrolase ◽  
Ligand Docking ◽  
Glycoside Hydrolase Family ◽  
Ligand Interactions ◽  
Tim Barrel ◽  
Branching Point ◽  
The Difference ◽  
Dynamics Simulations ◽  
And Function ◽  
Hydrolase Family

The structures of glycoside hydrolase family 17 (GH17) catalytic modules from modular proteins in the ndvB loci in Pseudomonas aeruginosa (Glt1), P. putida (Glt3) and Bradyrhizobium diazoefficiens (previously B. japonicum) (Glt20) were modeled to shed light on reported differences between these homologous transglycosylases concerning substrate size, preferred cleavage site (from reducing end (Glt20: DP2 product) or non-reducing end (Glt1, Glt3: DP4 products)), branching (Glt20) and linkage formed (1,3-linkage in Glt1, Glt3 and 1,6-linkage in Glt20). Hybrid models were built and stability of the resulting TIM-barrel structures was supported by molecular dynamics simulations. Catalytic amino acids were identified by superimposition of GH17 structures, and function was verified by mutagenesis using Glt20 as template (i.e., E120 and E209). Ligand docking revealed six putative subsites (−4, −3, −2, −1, +1 and +2), and the conserved interacting residues suggest substrate binding in the same orientation in all three transglycosylases, despite release of the donor oligosaccharide product from either the reducing (Glt20) or non-reducing end (Glt1, Gl3). Subsites +1 and +2 are most conserved and the difference in release is likely due to changes in loop structures, leading to loss of hydrogen bonds in Glt20. Substrate docking in Glt20 indicate that presence of covalently bound donor in glycone subsites −4 to −1 creates space to accommodate acceptor oligosaccharide in alternative subsites in the catalytic cleft, promoting a branching point and formation of a 1,6-linkage. The minimum donor size of DP5, can be explained assuming preferred binding of DP4 substrates in subsite −4 to −1, preventing catalysis.

Functional design of glycan-conjugated molecules using a chemoenzymatic approach

2021 ◽  
Author(s):  
Makoto Ogata
Keyword(s):  
Molecular Weight ◽  
Large Scale ◽  
Biological Activities ◽  
Natural Compounds ◽  
Low Molecular Weight ◽  
Functional Design ◽  
Enzymatic Method ◽  
Conjugated Molecules ◽  
Design Synthesis ◽  
And Function

Abstract Carbohydrates play important and diverse roles in the fundamental processes of life. We have established a method for accurately and a large scale synthesis of functional carbohydrates with diverse properties using a unique enzymatic method. Furthermore, various artificial glycan-conjugated molecules have been developed by adding these synthetic carbohydrates to macromolecules and to middle and low molecular weight molecules with different properties. These glycan-conjugated molecules have biological activities comparable to or higher than those of natural compounds, and present unique functions. In this review, several synthetic glycan-conjugated molecules are taken as examples to show design, synthesis and function.

A widespread pathway for substitution of adenine by diaminopurine in phage genomes

Science ◽  
2021 ◽  
Vol 372 (6541) ◽  
pp. 512-516
Author(s):  
Yan Zhou ◽  
Xuexia Xu ◽  
Yifeng Wei ◽  
Yu Cheng ◽  
Yu Guo ◽  
...  
Keyword(s):  
Large Scale ◽  
Restriction Enzymes ◽  
Diverse Range ◽  
Host Restriction ◽  
Form And Function ◽  
Multienzyme System ◽  
Dna Modifications ◽  
Dna Production ◽  
And Function

DNA modifications vary in form and function but generally do not alter Watson-Crick base pairing. Diaminopurine (Z) is an exception because it completely replaces adenine and forms three hydrogen bonds with thymine in cyanophage S-2L genomic DNA. However, the biosynthesis, prevalence, and importance of Z genomes remain unexplored. Here, we report a multienzyme system that supports Z-genome synthesis. We identified dozens of globally widespread phages harboring such enzymes, and we further verified the Z genome in one of these phages, Acinetobacter phage SH-Ab 15497, by using liquid chromatography with ultraviolet and mass spectrometry. The Z genome endows phages with evolutionary advantages for evading the attack of host restriction enzymes, and the characterization of its biosynthetic pathway enables Z-DNA production on a large scale for a diverse range of applications.

scholarly journals Tree Diversity Reduces Fungal Endophyte Richness and Diversity in a Large-Scale Temperate Forest Experiment

Diversity ◽  
2019 ◽  
Vol 11 (12) ◽  
pp. 234 ◽  
Author(s):  
Eric A. Griffin ◽  
Joshua G. Harrison ◽  
Melissa K. McCormick ◽  
Karin T. Burghardt ◽  
John D. Parker
Keyword(s):  
Phylogenetic Diversity ◽  
Large Scale ◽  
High Throughput Sequencing ◽  
Spatial Scales ◽  
Fungal Endophytes ◽  
Fungal Endophyte ◽  
Tree Diversity ◽  
Trophic Levels ◽  
Community Diversity ◽  
And Function

Although decades of research have typically demonstrated a positive correlation between biodiversity of primary producers and associated trophic levels, the ecological drivers of this association are poorly understood. Recent evidence suggests that the plant microbiome, or the fungi and bacteria found on and inside plant hosts, may be cryptic yet important drivers of important processes, including primary production and trophic interactions. Here, using high-throughput sequencing, we characterized foliar fungal community diversity, composition, and function from 15 broadleaved tree species (N = 545) in a recently established, large-scale temperate tree diversity experiment using over 17,000 seedlings. Specifically, we tested whether increases in tree richness and phylogenetic diversity would increase fungal endophyte diversity (the “Diversity Begets Diversity” hypothesis), as well as alter community composition (the “Tree Diversity–Endophyte Community” hypothesis) and function (the “Tree Diversity–Endophyte Function” hypothesis) at different spatial scales. We demonstrated that increasing tree richness and phylogenetic diversity decreased fungal species and functional guild richness and diversity, including pathogens, saprotrophs, and parasites, within the first three years of a forest diversity experiment. These patterns were consistent at the neighborhood and tree plot scale. Our results suggest that fungal endophytes, unlike other trophic levels (e.g., herbivores as well as epiphytic bacteria), respond negatively to increasing plant diversity.

scholarly journals Developing a vocabulary and ontology for modeling insect natural history data: example data, use cases, and competency questions

2019 ◽  
Vol 7 ◽  
Author(s):  
Brian Stucky ◽  
James Balhoff ◽  
Narayani Barve ◽  
Vijay Barve ◽  
Laura Brenskelle ◽  
...  
Keyword(s):  
Natural History ◽  
Large Scale ◽  
Use Cases ◽  
Data Systems ◽  
Multiple Sources ◽  
History Data ◽  
Insect Ecology ◽  
Many Sources ◽  
Multicellular Organisms ◽  
Initial Results

Insects are possibly the most taxonomically and ecologically diverse class of multicellular organisms on Earth. Consequently, they provide nearly unlimited opportunities to develop and test ecological and evolutionary hypotheses. Currently, however, large-scale studies of insect ecology, behavior, and trait evolution are impeded by the difficulty in obtaining and analyzing data derived from natural history observations of insects. These data are typically highly heterogeneous and widely scattered among many sources, which makes developing robust information systems to aggregate and disseminate them a significant challenge. As a step towards this goal, we report initial results of a new effort to develop a standardized vocabulary and ontology for insect natural history data. In particular, we describe a new database of representative insect natural history data derived from multiple sources (but focused on data from specimens in biological collections), an analysis of the abstract conceptual areas required for a comprehensive ontology of insect natural history data, and a database of use cases and competency questions to guide the development of data systems for insect natural history data. We also discuss data modeling and technology-related challenges that must be overcome to implement robust integration of insect natural history data.

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