scholarly journals Specificity of Molecular Fragments Binding to S100B versus S100A1 as Identified by NMR and Site Identification by Ligand Competitive Saturation (SILCS)

Molecules ◽  
2021 ◽  
Vol 26 (2) ◽  
pp. 381
Author(s):  
Brianna D. Young ◽  
Wenbo Yu ◽  
Darex J. Vera Rodríguez ◽  
Kristen M. Varney ◽  
Alexander D. MacKerell ◽  
...  
Keyword(s):  
Malignant Melanoma ◽  
Drug Design ◽  
Drug Targets ◽  
P53 Protein ◽  
S100 Protein ◽  
Data Sets ◽  
S100 Family ◽  
Promising Therapeutic Target ◽  
Site Identification ◽  
Insight Into

S100B, a biomarker of malignant melanoma, interacts with the p53 protein and diminishes its tumor suppressor function, which makes this S100 family member a promising therapeutic target for treating malignant melanoma. However, it is a challenge to design inhibitors that are specific for S100B in melanoma versus other S100-family members that are important for normal cellular activities. For example, S100A1 is most similar in sequence and structure to S100B, and this S100 protein is important for normal skeletal and cardiac muscle function. Therefore, a combination of NMR and computer aided drug design (CADD) was used to initiate the design of specific S100B inhibitors. Fragment-based screening by NMR, also termed “SAR by NMR,” is a well-established method, and was used to examine spectral perturbations in 2D [1H, 15N]-HSQC spectra of Ca2+-bound S100B and Ca2+-bound S100A1, side-by-side, and under identical conditions for comparison. Of the 1000 compounds screened, two were found to be specific for binding Ca2+-bound S100A1 and four were found to be specific for Ca2+-bound S100B, respectively. The NMR spectral perturbations observed in these six data sets were then used to model how each of these small molecule fragments showed specificity for one S100 versus the other using a CADD approach termed Site Identification by Ligand Competitive Saturation (SILCS). In summary, the combination of NMR and computational approaches provided insight into how S100A1 versus S100B bind small molecules specifically, which will enable improved drug design efforts to inhibit elevated S100B in melanoma. Such a fragment-based approach can be used generally to initiate the design of specific inhibitors for other highly homologous drug targets.

Sequence diversity and ligand-induced structural rearrangements of viper hyaluronidase

2016 ◽  
Vol 12 (4) ◽  
pp. 1128-1138 ◽  
Author(s):  
A. Christian Bharathi ◽  
Pradeep Kumar Yadav ◽  
B. Syed Ibrahim
Keyword(s):  
Drug Design ◽  
Cancer Treatment ◽  
Drug Targets ◽  
Structural Changes ◽  
Sequence Diversity ◽  
Structural Rearrangements ◽  
Structure Based Drug Design ◽  
Future Drug ◽  
Insight Into

The study focuses on ligand-induced structural changes of viper hyaluronidase and also provides insight into structure-based drug design for eukaryotic hyaluronidases, which could be future drug targets in cancer treatment, and venom spreading.

Recent Advances towards Drug Design Targeting the Protease of 2019 novel coronavirus (2019-nCoV)

2020 ◽  
Vol 27 ◽  
Author(s):  
Sehrish Bano ◽  
Abdul Hameed ◽  
Mariya Al-Rashida ◽  
Shafia Iftikhar ◽  
Jamshed Iqbal
Keyword(s):  
Drug Design ◽  
Rna Polymerase ◽  
Drug Targets ◽  
Antiviral Agents ◽  
Structural Features ◽  
Antiviral Drugs ◽  
Rna Dependent Rna Polymerase ◽  
Mortality And Morbidity ◽  
Functional Relationships ◽  
Novel Coronavirus

Background: The 2019 novel coronavirus (2019-nCoV), also known as coronavirus 2 (SARS-CoV-2) acute respiratory syndrome has recently emerged and continued to spread rapidly with high level of mortality and morbidity rates. Currently, no efficacious therapy is available to relieve coronavirus infections. As new drug design and development takes much time, there is a possibility to find an effective treatment from existing antiviral agents. Objective: In this case, there is a need to find out the relationship between possible drug targets and mechanism of action of antiviral drugs. This review discusses about the efforts to develop drug from known or new molecules. Methods: Viruses usually have two structural integrities, proteins and nucleic acids, both of which can be possible drug targets. Herein, we systemically discuss the structural-functional relationships of the spike, 3-chymotrypsin-like protease (3CLpro), papain like protease (PLpro) and RNA-dependent RNA polymerase (RdRp), as these are prominent structural features of corona virus. Certain antiviral drugs such as Remdesivir are RNA dependent RNA polymerase inhibitor. It has the ability to terminate RNA replication by inhibiting ATP. Results: It is reported that ATP is involved in synthesis of coronavirus non-structural proteins from 3CLpro and PLpro. Similarly, mechanisms of action of many other antiviral agents has been discussed in this review. It will provide new insights into the mechanism of inhibition, and let us develop new therapeutic antiviral approaches against novel SARS-CoV-2 coronavirus. Conclusion: In conclusion, this review summarizes recent progress in developing protease inhibitors for SARS-CoV-2.

Phytochemicals for drug discovery in Alzheimer’s disease: In silico Advances

2020 ◽  
Vol 26 ◽  
Author(s):  
Smriti Sharma ◽  
Vinayak Bhatia
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Drug Design ◽  
In Silico ◽  
Drug Targets ◽  
Development Process ◽  
Phytochemical Analysis ◽  
Computational Drug Design ◽  
Novel Drugs ◽  
Game Changer

: The search for novel drugs that can prevent or control Alzheimer’s disease has attracted lot of attention from researchers across the globe. Phytochemicals are increasingly being used to provide scaffolds to design drugs for AD. In silico techniques, have proven to be a game-changer in this drug design and development process. In this review, the authors have focussed on current advances in the field of in silico medicine, applied to phytochemicals, to discover novel drugs to prevent or cure AD. After giving a brief context of the etiology and available drug targets for AD, authors have discussed the latest advances and techniques in computational drug design of AD from phytochemicals. Some of the prototypical studies in this area are discussed in detail. In silico phytochemical analysis is a tool of choice for researchers all across the globe and helps integrate chemical biology with drug design.

scholarly journals Chromatin interactions reveal novel gene targets for drug repositioning in rheumatic diseases

2019 ◽  
Vol 78 (8) ◽  
pp. 1127-1134 ◽  
Author(s):  
Paul Martin ◽  
James Ding ◽  
Kate Duffus ◽  
Vasanthi Priyadarshini Gaddi ◽  
Amanda McGovern ◽  
...  
Keyword(s):  
Rheumatic Diseases ◽  
Pathway Analysis ◽  
Drug Targets ◽  
Drug Repositioning ◽  
Healthcare Providers ◽  
Cell Types ◽  
Data Sets ◽  
High Confidence ◽  
Causal Genes ◽  
Additional Cell

ObjectivesThere is a need to identify effective treatments for rheumatic diseases, and while genetic studies have been successful it is unclear which genes contribute to the disease. Using our existing Capture Hi-C data on three rheumatic diseases, we can identify potential causal genes which are targets for existing drugs and could be repositioned for use in rheumatic diseases.MethodsHigh confidence candidate causal genes were identified using Capture Hi-C data from B cells and T cells. These genes were used to interrogate drug target information from DrugBank to identify existing treatments, which could be repositioned to treat these diseases. The approach was refined using Ingenuity Pathway Analysis to identify enriched pathways and therefore further treatments relevant to the disease.ResultsOverall, 454 high confidence genes were identified. Of these, 48 were drug targets (108 drugs) and 11 were existing therapies used in the treatment of rheumatic diseases. After pathway analysis refinement, 50 genes remained, 13 of which were drug targets (33 drugs). However considering targets across all enriched pathways, a further 367 drugs were identified for potential repositioning.ConclusionCapture Hi-C has the potential to identify therapies which could be repositioned to treat rheumatic diseases. This was particularly successful for rheumatoid arthritis, where six effective, biologic treatments were identified. This approach may therefore yield new ways to treat patients, enhancing their quality of life and reducing the economic impact on healthcare providers. As additional cell types and other epigenomic data sets are generated, this prospect will improve further.

scholarly journals High-Performance Computing of BEAST/BEAGLE in Bayesian Phylogenetics using SDumont Hybrid Resources

2020 ◽  
Author(s):  
Kary Ocaña ◽  
Micaella Coelho ◽  
Guilherme Freire ◽  
Carla Osthoff
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Current Knowledge ◽  
Data Sets ◽  
Length Estimation ◽  
Computationally Intensive ◽  
Mcmc Chain ◽  
Performance Computing ◽  
Insight Into

Bayesian phylogenetic algorithms are computationally intensive. BEAST 1.10 inferences made use of the BEAGLE 3 high-performance library for efficient likelihood computations. The strategy allows phylogenetic inference and dating in current knowledge for SARS-CoV-2 transmission. Follow-up simulations on hybrid resources of Santos Dumont supercomputer using four phylogenomic data sets, we characterize the scaling performance behavior of BEAST 1.10. Our results provide insight into the species tree and MCMC chain length estimation, identifying preferable requirements to improve the use of high-performance computing resources. Ongoing steps involve analyzes of SARS-CoV-2 using BEAST 1.8 in multi-GPUs.

scholarly journals Pathway-guided deep neural network toward interpretable and predictive modeling of drug sensitivity

2020 ◽  
Author(s):  
Lei Deng ◽  
Yideng Cai ◽  
Wenhao Zhang ◽  
Wenyi Yang ◽  
Bo Gao ◽  
...  
Keyword(s):  
Neural Network ◽  
Cancer Cells ◽  
Drug Targets ◽  
Deep Neural Network ◽  
Drug Sensitivity ◽  
Predictive Ability ◽  
Biological Knowledge ◽  
Data Sets ◽  
Source Codes ◽  
Sensitivity Data

AbstractMotivationTo efficiently save cost and reduce risk in drug research and development, there is a pressing demand to develop in-silico methods to predict drug sensitivity to cancer cells. With the exponentially increasing number of multi-omics data derived from high-throughput techniques, machine learning-based methods have been applied to the prediction of drug sensitivities. However, these methods have drawbacks either in the interpretability of mechanism of drug action or limited performance in modeling drug sensitivity.ResultsIn this paper, we presented a pathway-guided deep neural network model, referred to as pathDNN, to predict the drug sensitivity to cancer cells. Biological pathways describe a group of molecules in a cell that collaborates to control various biological functions like cell proliferation and death, thereby abnormal function of pathways can result in disease. To make advantage of both the excellent predictive ability of deep neural network and the biological knowledge of pathways, we reshape the canonical DNN structure by incorporating a layer of pathway nodes and their connections to input gene nodes, which makes the DNN model more interpretable and predictive compared to canonical DNN. We have conducted extensive performance evaluations on multiple independent drug sensitivity data sets, and demonstrate that pathDNN significantly outperformed canonical DNN model and seven other classical regression models. Most importantly, we observed remarkable activity decreases of disease-related pathway nodes during forward propagation upon inputs of drug targets, which implicitly corresponds to the inhibition effect of disease-related pathways induced by drug treatment on cancer cells. Our empirical experiments show that pathDNN achieves pharmacological interpretability and predictive ability in modeling drug sensitivity to cancer cells.AvailabilityThe web server, as well as the processed data sets and source codes for reproducing our work, is available at http://pathdnn.denglab.org

scholarly journals Informative Frailty Indices from Binarized Biomarkers

2020 ◽  
Author(s):  
Garrett Stubbings ◽  
Spencer Farrell ◽  
Arnold Mitnitski ◽  
Kenneth Rockwood ◽  
Andrew Rutenberg
Keyword(s):  
Medical Knowledge ◽  
Data Sets ◽  
Canadian Study ◽  
Adverse Health Outcomes ◽  
Health And Nutrition ◽  
Health Measure ◽  
Upper Limits ◽  
Insight Into ◽  
Better Than ◽  
Biomarker Data

AbstractFrailty indices (FI) based on continuous valued health data, such as obtained from blood and urine tests, have been shown to be predictive of adverse health outcomes. However, creating FI from such biomarker data requires a binarization treatment that is difficult to standardize across studies. In this work, we explore a “quantile” methodology for the generic treatment of biomarker data that allows us to construct an FI without preexisting medical knowledge (i.e. risk thresholds) of the included biomarkers. We show that our quantile approach performs as well as, or even slightly better than, established methods for the National Health and Nutrition Examination Survey (NHANES) and the Canadian Study of Health and Aging (CSHA) data sets. Furthermore, we show that our approach is robust to cohort effects within studies as compared to other data-based methods. The success of our binarization approaches provides insight into the robustness of the FI as a health measure, the upper limits of the FI observed in various data sets, and highlights general difficulties in obtaining absolute scales for comparing FI between studies.

scholarly journals Panoramic stitching of heterogeneous single-cell transcriptomic data

2018 ◽  
Author(s):  
Brian Hie ◽  
Bryan Bryson ◽  
Bonnie Berger
Keyword(s):  
Single Cell ◽  
Cell Types ◽  
Data Sets ◽  
Cell Type ◽  
Data Set ◽  
Wide Range ◽  
Data Set Integration ◽  
Biological Patterns ◽  
Insight Into ◽  
Comprehensive Reference

AbstractResearchers are generating single-cell RNA sequencing (scRNA-seq) profiles of diverse biological systems1–4 and every cell type in the human body.5 Leveraging this data to gain unprecedented insight into biology and disease will require assembling heterogeneous cell populations across multiple experiments, laboratories, and technologies. Although methods for scRNA-seq data integration exist6,7, they often naively merge data sets together even when the data sets have no cell types in common, leading to results that do not correspond to real biological patterns. Here we present Scanorama, inspired by algorithms for panorama stitching, that overcomes the limitations of existing methods to enable accurate, heterogeneous scRNA-seq data set integration. Our strategy identifies and merges the shared cell types among all pairs of data sets and is orders of magnitude faster than existing techniques. We use Scanorama to combine 105,476 cells from 26 diverse scRNA-seq experiments across 9 different technologies into a single comprehensive reference, demonstrating how Scanorama can be used to obtain a more complete picture of cellular function across a wide range of scRNA-seq experiments.

scholarly journals ASSESSING LIDAR TRAINING DATA QUANTITIES FOR CLASSIFICATION MODELS

2021 ◽  
Vol XLVI-4/W4-2021 ◽  
pp. 101-106
Author(s):  
O. Majgaonkar ◽  
K. Panchal ◽  
D. Laefer ◽  
M. Stanley ◽  
Y. Zaki
Keyword(s):  
Point Cloud ◽  
Classification Performance ◽  
Training Data ◽  
Lidar Data ◽  
Data Sets ◽  
Classification Models ◽  
Fundamental Properties ◽  
Point Cloud Classification ◽  
The Impact ◽  
Insight Into

Abstract. Classifying objects within aerial Light Detection and Ranging (LiDAR) data is an essential task to which machine learning (ML) is applied increasingly. ML has been shown to be more effective on LiDAR than imagery for classification, but most efforts have focused on imagery because of the challenges presented by LiDAR data. LiDAR datasets are of higher dimensionality, discontinuous, heterogenous, spatially incomplete, and often scarce. As such, there has been little examination into the fundamental properties of the training data required for acceptable performance of classification models tailored for LiDAR data. The quantity of training data is one such crucial property, because training on different sizes of data provides insight into a model’s performance with differing data sets. This paper assesses the impact of training data size on the accuracy of PointNet, a widely used ML approach for point cloud classification. Subsets of ModelNet ranging from 40 to 9,843 objects were validated on a test set of 400 objects. Accuracy improved logarithmically; decelerating from 45 objects onwards, it slowed significantly at a training size of 2,000 objects, corresponding to 20,000,000 points. This work contributes to the theoretical foundation for development of LiDAR-focused models by establishing a learning curve, suggesting the minimum quantity of manually labelled data necessary for satisfactory classification performance and providing a path for further analysis of the effects of modifying training data characteristics.

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