scholarly journals A Large-Scale Structural Classification of Antimicrobial Peptides

2015 ◽  
Vol 2015 ◽  
pp. 1-6 ◽  
Author(s):  
Hao-Ting Lee ◽  
Chen-Che Lee ◽  
Je-Ruei Yang ◽  
Jim Z. C. Lai ◽  
Kuan Y. Chang
Keyword(s):  
Antimicrobial Peptides ◽  
Large Scale ◽  
Easy Access ◽  
Protein Fold ◽  
Structural Classification ◽  
Drug Candidates ◽  
Partial Classification ◽  
Potent Drug ◽  
Protein Fold Space

Antimicrobial peptides (AMPs) are potent drug candidates against microbial organisms such as bacteria, fungi, parasites, and viruses. AMPs have abundant sequences and structures, two fundamental resources for bioinformatics researches, but analyses on how they associate with each other are either nonexistent or limited to partial classification and data. We thus present A Database of Anti-Microbial peptides (ADAM), which contains 7,007 unique sequences and 759 structures, to systematically establish comprehensive associations between AMP sequences and structures through structural folds and to provide an easy access to view their relationships. 30 distinct AMP structural fold clusters with more than one structure are detected and about a thousand AMPs are associated with at least one structural fold cluster. According to ADAM, AMP structural folds are limited—AMPs only cover about 3% of the overall protein fold space.

scholarly journals Lessons from making the Structural Classification of Proteins (SCOP) and their implications for protein structure modelling

2016 ◽  
Vol 44 (3) ◽  
pp. 937-943 ◽  
Author(s):  
Antonina Andreeva
Keyword(s):  
Protein Structure ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Large Scale ◽  
Structural Variations ◽  
Structural Classification ◽  
Alternative Structures ◽  
Genome Annotations ◽  
Structural Classification Of Proteins

The Structural Classification of Proteins (SCOP) database has facilitated the development of many tools and algorithms and it has been successfully used in protein structure prediction and large-scale genome annotations. During the development of SCOP, numerous exceptions were found to topological rules, along with complex evolutionary scenarios and peculiarities in proteins including the ability to fold into alternative structures. This article reviews cases of structural variations observed for individual proteins and among groups of homologues, knowledge of which is essential for protein structure modelling.

scholarly journals NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products

2020 ◽  
Author(s):  
Hyunwoo kim ◽  
Mingxun Wang ◽  
Christopher Leber ◽  
Louis-Felix Nothias ◽  
Raphael Reher ◽  
...  
Keyword(s):  
Natural Products ◽  
Large Scale ◽  
Biosynthetic Pathway ◽  
Biological Properties ◽  
Massive Data ◽  
Structural Classification ◽  
Classification Framework ◽  
Semantic Ontology ◽  
Classification Tool

<div> <div> <div> <p>Computational approaches such as genome and metabolome mining are becoming essential to natural products (NP) research. Consequently, demands for automated NP classification system for massive data are increasing. The semantic ontology of NPs classifies molecules based on the taxonomy of the producing organism, the nature of the biosynthetic pathway, their biological properties, as well as the presence of chemical substructures. Thus, a holistic and automatic NP classification framework could have considerable value to comprehensively navigate the relatedness of NPs. Here, we introduce NPClassifier, the first deep-learning tool for the automated structural classification of NPs. We expect that NPClassifier will accelerate NP discovery by facilitating and enabling large-scale genome and metabolome mining efforts and linking of NP structures to their underlying bioactivity. </p> </div> </div> </div>

scholarly journals NPClassifier: A Deep Neural Network-Based Structural Classification Tool for Natural Products

2020 ◽  
Author(s):  
Hyunwoo kim ◽  
Mingxun Wang ◽  
Christopher Leber ◽  
Louis-Felix Nothias ◽  
Raphael Reher ◽  
...  
Keyword(s):  
Natural Products ◽  
Large Scale ◽  
Biosynthetic Pathway ◽  
Biological Properties ◽  
Massive Data ◽  
Structural Classification ◽  
Classification Framework ◽  
Semantic Ontology ◽  
Classification Tool

<div> <div> <div> <p>Computational approaches such as genome and metabolome mining are becoming essential to natural products (NP) research. Consequently, demands for automated NP classification system for massive data are increasing. The semantic ontology of NPs classifies molecules based on the taxonomy of the producing organism, the nature of the biosynthetic pathway, their biological properties, as well as the presence of chemical substructures. Thus, a holistic and automatic NP classification framework could have considerable value to comprehensively navigate the relatedness of NPs. Here, we introduce NPClassifier, the first deep-learning tool for the automated structural classification of NPs. We expect that NPClassifier will accelerate NP discovery by facilitating and enabling large-scale genome and metabolome mining efforts and linking of NP structures to their underlying bioactivity. </p> </div> </div> </div>

Classification of woody-shrub vegetation of the forest-steppe complex of Privolzhskaya Upland

2009 ◽  
pp. 27-53
Author(s):  
A. Yu. Kudryavtsev
Keyword(s):  
Plant Community ◽  
Plant Communities ◽  
Dominant Species ◽  
Large Scale ◽  
Nature Reserve ◽  
Vegetation Mapping ◽  
Vegetation Classification ◽  
Forest Steppe ◽  
Mapping Data

Diversity of plant communities in the nature reserve “Privolzhskaya Forest-Steppe”, Ostrovtsovsky area, is analyzed on the basis of the large-scale vegetation mapping data from 2000. The plant community classi­fication based on the Russian ecologic-phytocoenotic approach is carried out. 12 plant formations and 21 associations are distinguished according to dominant species and a combination of ecologic-phytocoenotic groups of species. A list of vegetation classification units as well as the characteristics of theshrub and woody communities are given in this paper.

Classification of vegetation in Cucphuong National Park with the aim of large-scale mapping, Vietnam

1996 ◽  
pp. 64-67 ◽  
Author(s):  
Nguen Nghia Thin ◽  
Nguen Ba Thu ◽  
Tran Van Thuy
Keyword(s):  
National Park ◽  
Large Scale ◽  
Satellite Images ◽  
Field Work ◽  
Aerial Photographs ◽  
Vegetation Map ◽  
Dense Forest ◽  
Broad Leaved Forest ◽  
Leaved Forest

The tropical seasonal rainy evergreen broad-leaved forest vegetation of the Cucphoung National Park has been classified and the distribution of plant communities has been shown on the map using the relations of vegetation to geology, geomorphology and pedology. The method of vegetation mapping includes: 1) the identifying of vegetation types in the remote-sensed materials (aerial photographs and satellite images); 2) field work to compile the interpretation keys and to characterize all the communities of a study area; 3) compilation of the final vegetation map using the combined information. In the classification presented a number of different level vegetation units have been identified: formation classes (3), formation sub-classes (3), formation groups (3), formations (4), subformations (10) and communities (19). Communities have been taken as mapping units. So in the vegetation map of the National Park 19 vegetation categories has been shown altogether, among them 13 are natural primary communities, and 6 are the secondary, anthropogenic ones. The secondary succession goes through 3 main stages: grassland herbaceous xerophytic vegetation, xerophytic scrub, dense forest.

Recent Advances in the Development of Antimicrobial Peptides (AMPs): Attempts for Sustainable Medicine?

2018 ◽  
Vol 25 (21) ◽  
pp. 2503-2519 ◽  
Author(s):  
Anne Kokel ◽  
Marianna Torok
Keyword(s):  
Side Effects ◽  
Antimicrobial Peptides ◽  
Potential Drug ◽  
Green Technologies ◽  
Specific Antibodies ◽  
Structural Modifications ◽  
Drug Candidates ◽  
Induce Resistance ◽  
Conventional Antibiotics

Background: Since the first isolation of antimicrobial peptides (AMPs) they have attracted extensive interest in medicinal chemistry. However, only a few AMP-based drugs are currently available on the market. Despite their effectiveness, biodegradability, and versatile mode of action that is less likely to induce resistance compared to conventional antibiotics, AMPs suffer from major issues that need to be addressed to broaden their use. Notably, AMPs can lack selectivity leading to side effects and cytotoxicity, and also exhibit in vivo instability. Several strategies are being actively considered to overcome the limitations that restrain the success of AMPs. Methods: In the current work, recent strategies reported for improving AMPs in the context of drug design and delivery were surveyed, and also their possible impact on patients and the environment was assessed. Results: As a major advantage AMPs possess an easily tunable skeleton offering opportunities to improve their properties. Strategic structural modifications and the beneficial properties of cyclic or branched AMPs in term of stability have been reported. The conjugation of AMPs with nanoparticles has also been explored to increase their in vivo stability. Other techniques such as the coupling of AMPs with specific antibodies aim to increase the selectivity of the potential drug towards the target. These strategies were evaluated for their effect on the environment highlighting green technologies. Conclusion: Although further research is needed taking into account both environmental and human health consequences of novel AMPs, several of these compounds are promising drug candidates for use in sustainable medicine.

The Swiss Prison Study (SWIPS): Protocol for Establishing a Public Health Registry of Prisoners in Switzerland (Preprint)

2020 ◽  
Author(s):  
Thomas Gaisl ◽  
Naser Musli ◽  
Patrick Baumgartner ◽  
Marc Meier ◽  
Silvana K Rampini ◽  
...  
Keyword(s):  
Public Health ◽  
Medical History ◽  
Large Scale ◽  
International Classification Of Diseases ◽  
Disease Prevalence ◽  
Vulnerable Population ◽  
International Classification ◽  
Classification Of Diseases ◽  
Health Registry

BACKGROUND The health aspects, disease frequencies, and specific health interests of prisoners and refugees are poorly understood. Importantly, access to the health care system is limited for this vulnerable population. There has been no systematic investigation to understand the health issues of inmates in Switzerland. Furthermore, little is known on how recent migration flows in Europe may have affected the health conditions of inmates. OBJECTIVE The Swiss Prison Study (SWIPS) is a large-scale observational study with the aim of establishing a public health registry in northern-central Switzerland. The primary objective is to establish a central database to assess disease prevalence (ie, International Classification of Diseases-10 codes [German modification]) among prisoners. The secondary objectives include the following: (1) to compare the 2015 versus 2020 disease prevalence among inmates against a representative sample from the local resident population, (2) to assess longitudinal changes in disease prevalence from 2015 to 2020 by using cross-sectional medical records from all inmates at the Police Prison Zurich, Switzerland, and (3) to identify unrecognized health problems to prepare successful public health strategies. METHODS Demographic and health-related data such as age, sex, country of origin, duration of imprisonment, medication (including the drug name, brand, dosage, and release), and medical history (including the International Classification of Diseases-10 codes [German modification] for all diagnoses and external results that are part of the medical history in the prison) have been deposited in a central register over a span of 5 years (January 2015 to August 2020). The final cohort is expected to comprise approximately 50,000 to 60,000 prisoners from the Police Prison Zurich, Switzerland. RESULTS This study was approved on August 5, 2019 by the ethical committee of the Canton of Zurich with the registration code KEK-ZH No. 2019-01055 and funded in August 2020 by the “Walter and Gertrud Siegenthaler” foundation and the “Theodor and Ida Herzog-Egli” foundation. This study is registered with the International Standard Randomized Controlled Trial Number registry. Data collection started in August 2019 and results are expected to be published in 2021. Findings will be disseminated through scientific papers as well as presentations and public events. CONCLUSIONS This study will construct a valuable database of information regarding the health of inmates and refugees in Swiss prisons and will act as groundwork for future interventions in this vulnerable population. CLINICALTRIAL ISRCTN registry ISRCTN11714665; http://www.isrctn.com/ISRCTN11714665 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/23973

Deep Learning-Based Classification of Large-Scale Airborne LiDAR Point Cloud

2021 ◽  
pp. 1-15
Author(s):  
Mathieu Turgeon-Pelchat ◽  
Samuel Foucher ◽  
Yacine Bouroubi
Keyword(s):  
Deep Learning ◽  
Point Cloud ◽  
Large Scale ◽  
Airborne Lidar
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