scholarly journals LIGHTHOUSE illuminates therapeutics for a variety of diseases including COVID-19

2021 ◽  
Author(s):  
Hideyuki Shimizu ◽  
Manabu Kodama ◽  
Masaki Matsumoto ◽  
Yasuko Orba ◽  
Michihito Sasaki ◽  
...  
Keyword(s):  
Paradigm Shift ◽  
High Throughput Screening ◽  
Structural Information ◽  
Protein Structures ◽  
Therapeutic Targets ◽  
Basic Research ◽  
Human Diseases ◽  
Docking Simulations ◽  
Research Findings ◽  
Therapeutic Compounds

SUMMARYAlthough numerous promising therapeutic targets for human diseases have been discovered, most have not been successfully translated into clinical practice1. A bottleneck in the application of basic research findings to patients is the enormous cost, time, and effort required for high-throughput screening of potential drugs2 for given therapeutic targets. Recent advances in 3D docking simulations have not solved this problem, given that 3D protein structures with sufficient resolution are not always available and that they are computationally expensive to obtain. Here we have developed LIGHTHOUSE, a graph-based deep learning approach for discovery of the hidden principles underlying the association of small-molecule compounds with target proteins, and we present its validation by identifying potential therapeutic compounds for various human diseases. Without any 3D structural information for proteins or chemicals, LIGHTHOUSE estimates protein-compound scores that incorporate known evolutionary relations and available experimental data. It identified novel therapeutics for cancer, lifestyle-related disease, and bacterial infection. Moreover, LIGHTHOUSE predicted ethoxzolamide as a therapeutic for coronavirus disease 2019 (COVID-19), and this agent was indeed effective against alpha, beta, gamma, and delta variants of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) that are rampant worldwide. Given that ethoxzolamide is already approved for several diseases, it could be rapidly deployed for the treatment of patients with COVID-19. We envision that LIGHTHOUSE will bring about a paradigm shift in translational medicine, providing a bridge from bench side to bedside.

scholarly journals Pulp-dentin regeneration: current approaches and challenges

2019 ◽  
Vol 10 ◽  
pp. 204173141881926 ◽  
Author(s):  
Chanyong Jung ◽  
Sangwan Kim ◽  
Taeuk Sun ◽  
Yong-Bum Cho ◽  
Minju Song
Keyword(s):  
Paradigm Shift ◽  
Basic Research ◽  
Immune Defense ◽  
Permanent Teeth ◽  
Clinical Approach ◽  
Defense System ◽  
Pulp Tissue ◽  
Research Findings ◽  
Preclinical And Clinical Studies ◽  
Clinical Conditions

Regenerative endodontic procedures for immature permanent teeth with apical periodontitis confer biological advantages such as tooth homeostasis, enhanced immune defense system, and a functional pulp-dentin complex, in addition to clinical advantages such as the facilitation of root development. Currently, this procedure is recognized as a paradigm shift from restoration using materials to regenerate pulp-dentin tissues. Many studies have been conducted with regard to stem/progenitor cells, scaffolds, and biomolecules, associated with pulp tissue engineering. However, preclinical and clinical studies have evidently revealed several drawbacks in the current clinical approach to revascularization that may lead to unfavorable outcomes. Therefore, our review examines the challenges encountered under clinical conditions and summarizes current research findings in an attempt to provide direction for transition from basic research to clinical practice.

scholarly journals A graph-based algorithm for detecting rigid domains in protein structures

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Truong Khanh Linh Dang ◽  
Thach Nguyen ◽  
Michael Habeck ◽  
Mehmet Gültas ◽  
Stephan Waack
Keyword(s):  
Structural Changes ◽  
Viterbi Algorithm ◽  
Structural Information ◽  
Line Graph ◽  
Clustering Algorithms ◽  
Protein Structures ◽  
Alternative Methods ◽  
Structural Transitions ◽  
Tuning Parameters ◽  
Conformational Ensembles

Abstract Background Conformational transitions are implicated in the biological function of many proteins. Structural changes in proteins can be described approximately as the relative movement of rigid domains against each other. Despite previous efforts, there is a need to develop new domain segmentation algorithms that are capable of analysing the entire structure database efficiently and do not require the choice of protein-dependent tuning parameters such as the number of rigid domains. Results We develop a graph-based method for detecting rigid domains in proteins. Structural information from multiple conformational states is represented by a graph whose nodes correspond to amino acids. Graph clustering algorithms allow us to reduce the graph and run the Viterbi algorithm on the associated line graph to obtain a segmentation of the input structures into rigid domains. In contrast to many alternative methods, our approach does not require knowledge about the number of rigid domains. Moreover, we identified default values for the algorithmic parameters that are suitable for a large number of conformational ensembles. We test our algorithm on examples from the DynDom database and illustrate our method on various challenging systems whose structural transitions have been studied extensively. Conclusions The results strongly suggest that our graph-based algorithm forms a novel framework to characterize structural transitions in proteins via detecting their rigid domains. The web server is available at http://azifi.tz.agrar.uni-goettingen.de/webservice/.

scholarly journals Osteosarcoma Pathogenesis Leads the Way to New Target Treatments

2021 ◽  
Vol 22 (2) ◽  
pp. 813
Author(s):  
Isabel Fernandes ◽  
Cecília Melo-Alvim ◽  
Raquel Lopes-Brás ◽  
Miguel Esperança-Martins ◽  
Luís Costa
Keyword(s):  
Clinical Trials ◽  
Phase I ◽  
Poor Prognosis ◽  
Therapeutic Targets ◽  
Rare Condition ◽  
Basic Research ◽  
Metastatic Setting ◽  
Phase I And Ii ◽  
The Way

Osteosarcoma (OS) is a rare condition with very poor prognosis in a metastatic setting. Basic research has enabled a better understanding of OS pathogenesis and the discovery of new potential therapeutic targets. Phase I and II clinical trials are already ongoing, with some promising results for these patients. This article reviews OS pathogenesis and new potential therapeutic targets.

scholarly journals Reinforcement Learning to Boost Molecular Docking upon Protein Conformational Ensemble

2021 ◽  
Author(s):  
Bin Chong ◽  
Yingguang Yang ◽  
Zi-Le Wang ◽  
Han Xing ◽  
Zhirong Liu
Keyword(s):  
Molecular Docking ◽  
Reinforcement Learning ◽  
Intrinsically Disordered Proteins ◽  
Therapeutic Targets ◽  
Human Diseases ◽  
Disordered Proteins ◽  
Conformational Ensemble ◽  
Intrinsically Disordered

Intrinsically disordered proteins (IDPs) widely involve in human diseases and are thus attractive therapeutic targets. In practice, however, it is computationally prohibitive to dock large ligand libraries to thousands and...

scholarly journals Application of microRNA Database Mining in Biomarker Discovery and Identification of Therapeutic Targets for Complex Disease

2020 ◽  
Vol 4 (1) ◽  
pp. 5
Author(s):  
Jennifer L. Major ◽  
Rushita A. Bagchi ◽  
Julie Pires da Silva
Keyword(s):  
Complex Disease ◽  
Biomarker Discovery ◽  
Ischemia Reperfusion Injury ◽  
Ischemia Reperfusion ◽  
Expression Patterns ◽  
Therapeutic Targets ◽  
Human Diseases ◽  
Mirna Expression Data ◽  
Artery Disease ◽  
Underlying Mechanisms

Over the past two decades, it has become increasingly evident that microRNAs (miRNA) play a major role in human diseases such as cancer and cardiovascular diseases. Moreover, their easy detection in circulation has made them a tantalizing target for biomarkers of disease. This surge in interest has led to the accumulation of a vast amount of miRNA expression data, prediction tools, and repositories. We used the Human microRNA Disease Database (HMDD) to discover miRNAs which shared expression patterns in the related diseases of ischemia/reperfusion injury, coronary artery disease, stroke, and obesity as a model to identify miRNA candidates for biomarker and/or therapeutic intervention in complex human diseases. Our analysis identified a single miRNA, hsa-miR-21, which was casually linked to all four pathologies, and numerous others which have been detected in the circulation in more than one of the diseases. Target analysis revealed that hsa-miR-21 can regulate a number of genes related to inflammation and cell growth/death which are major underlying mechanisms of these related diseases. Our study demonstrates a model for researchers to use HMDD in combination with gene analysis tools to identify miRNAs which could serve as biomarkers and/or therapeutic targets of complex human diseases.

scholarly journals Expanding our knowledge of the protein universe: Modelling of protein structures

2014 ◽  
Vol 70 (a1) ◽  
pp. C491-C491
Author(s):  
Jürgen Haas ◽  
Alessandro Barbato ◽  
Tobias Schmidt ◽  
Steven Roth ◽  
Andrew Waterhouse ◽  
...  
Keyword(s):  
Computational Modeling ◽  
Structure Prediction ◽  
Structural Information ◽  
Protein Structures ◽  
Model Organism ◽  
Data Bank ◽  
Continuous Model ◽  
Structure Modeling ◽  
Structure Comparison ◽  
Modeling And Prediction

Computational modeling and prediction of three-dimensional macromolecular structures and complexes from their sequence has been a long standing goal in structural biology. Over the last two decades, a paradigm shift has occurred: starting from a large "knowledge gap" between the huge number of protein sequences compared to a small number of experimentally known structures, today, some form of structural information – either experimental or computational – is available for the majority of amino acids encoded by common model organism genomes. Methods for structure modeling and prediction have made substantial progress of the last decades, and template based homology modeling techniques have matured to a point where they are now routinely used to complement experimental techniques. However, computational modeling and prediction techniques often fall short in accuracy compared to high-resolution experimental structures, and it is often difficult to convey the expected accuracy and structural variability of a specific model. Retrospectively assessing the quality of blind structure prediction in comparison to experimental reference structures allows benchmarking the state-of-the-art in structure prediction and identifying areas which need further development. The Critical Assessment of Structure Prediction (CASP) experiment has for the last 20 years assessed the progress in the field of protein structure modeling based on predictions for ca. 100 blind prediction targets per experiment which are carefully evaluated by human experts. The "Continuous Model EvaluatiOn" (CAMEO) project aims to provide a fully automated blind assessment for prediction servers based on weekly pre-released sequences of the Protein Data Bank PDB. CAMEO has been made possible by the development of novel scoring methods such as lDDT, which are robust against domain movements to allow for automated continuous structure comparison without human intervention.

scholarly journals Child Witness Expressions of Certainty are Informative

2019 ◽  
Author(s):  
Alice Winsor ◽  
Heather D Flowe ◽  
Travis Morgan Seale-Carlisle ◽  
Isabella Killeen ◽  
Danielle Hett ◽  
...  
Keyword(s):  
Applied Research ◽  
Basic Research ◽  
Implicit Measure ◽  
Detection Model ◽  
List Learning ◽  
Research Findings ◽  
Basic And Applied Research ◽  
Witness Literature ◽  
Police Lineup ◽  
Accuracy Relationship

Children are frequently witnesses of crime. In the witness literature and legal systems, children are often deemed to have unreliable memories. Yet, in the basic developmental literature, young children can monitor their memory. To address these contradictory conclusions, we reanalysed the confidence-accuracy relationship in basic and applied research. Confidence provided considerable information about memory accuracy, from at least age 8, but possibly younger. We also conducted an experiment where children in young- (4–6 years), middle- (7–9 years), and late- (10–17 years) childhood (N=2,205) watched a person in a video, and then identified that person from a police lineup. Children provided a confidence rating (an explicit judgement), and used an interactive lineup—in which the lineup faces can be rotated—and we analyzed children’s viewing behavior (an implicit measure of metacognition). A strong confidence-accuracy relationship was observed from age 10, and an emerging relationship from age 7. A constant likelihood ratio signal-detection model can be used to understand these findings. Moreover, in all ages, interactive viewing behavior differed in children who made correct versus incorrect suspect identifications. Our research reconciles the apparent divide between applied and basic research findings and suggests that the fundamental architecture of metacognition that has previously been evidenced in basic list-learning paradigms also underlies performance on complex applied tasks. Contrary to what is believed by legal practitioners, but similar to what has been found in the basic literature, identifications made by children can be reliable when appropriate metacognitive measures are used to estimate accuracy.

Protein structure by solid-state NMR spectroscopy

1987 ◽  
Vol 19 (1-2) ◽  
pp. 7-49 ◽  
Author(s):  
S. J. Opella ◽  
P. L. Stewart ◽  
K. G. Valentine
Keyword(s):  
Solid State ◽  
Nmr Spectroscopy ◽  
Solid State Nmr ◽  
Structural Information ◽  
Biological Systems ◽  
Genetic Regulation ◽  
Protein Structures ◽  
Crystalline Solid ◽  
Solid State Nmr Spectroscopy ◽  
Nmr Methods

The three-dimensional structures of proteins are among the most valuable contributions of biophysics to the understanding of biological systems (Dickerson & Geis, 1969; Creighton, 1983). Protein structures are utilized in the description and interpretation of a wide variety of biological phenomena, including genetic regulation, enzyme mechanisms, antibody recognition, cellular energetics, and macroscopic mechanical and structural properties of molecular assemblies. Virtually all of the information currently available about the structures of proteins at atomic resolution has been obtained from diffraction studies of single crystals of proteins (Wyckoff et al, 1985). However, recently developed NMR methods are capable of determining the structures of proteins and are now being applied to a variety of systems, including proteins in solution and other non-crystalline environments that are not amenable for X-ray diffraction studies. Solid-state NMR methods are useful for proteins that undergo limited overall reorientation by virtue of their being in the crystalline solid state or integral parts of supramolecular structures that do not reorient rapidly in solution. For reviews of applications of solid-state NMR spectroscopy to biological systems see Torchia and VanderHart (1979), Griffin (1981), Oldfield et al. (1982), Opella (1982), Torchia (1982), Gauesh (1984), Torchia (1984) and Opella (1986). This review describes how solid-state NMR can be used to obtain structural information about proteins. Methods applicable to samples with macroscopic orientation are emphasized.

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