scholarly journals Computational Tools for Strain Optimization by Adding Reactions

2012 ◽  
pp. 241-250
Author(s):  
Sara Correia ◽  
Miguel Rocha
Keyword(s):  
Computational Tools ◽  
Strain Optimization

Optical Properties of Photodynamic Therapy Drugs in Different Environments: The Paradigmatic Case of Temoporfin

2020 ◽  
Author(s):  
busenur Aslanoglu ◽  
Ilya Yakavets ◽  
Vladimir Zorin ◽  
Henri-Pierre Lassalle ◽  
Francesca Ingrosso ◽  
...  
Keyword(s):  
Drug Delivery ◽  
Optical Properties ◽  
Photodynamic Therapy ◽  
Minimally Invasive ◽  
Visible Light ◽  
Cancer Treatment ◽  
Singlet Oxygen ◽  
Tumor Cells ◽  
Molecular Oxygen ◽  
Computational Tools

Computational tools have been used to study the photophysical and photochemical features of photosensitizers in photodynamic therapy (PDT) –a minimally invasive, less aggressive alternative for cancer treatment. PDT is mainly based by the activation of molecular oxygen through the action of a photoexcited sensitizer (photosensitizer). Temoporfin, widely known as mTHPC, is a second-generation photosensitizer, which produces the cytotoxic singlet oxygen when irradiated with visible light and hence destroys tumor cells. However, the bioavailability of the mostly hydrophobic photosensitizer, and hence its incorporation into the cells, is fundamental to achieve the desired effect on malignant tissues by PDT. In this study, we focus on the optical properties of the temoporfin chromophore in different environments –in <i>vacuo</i>, in solution, encapsulated in drug delivery agents, namely cyclodextrin, and interacting with a lipid bilayer.

Web-based Tools for Drug Repurposing: Successful Examples of Collaborative Research

2020 ◽  
Vol 28 (1) ◽  
pp. 181-195
Author(s):  
Quentin Vanhaelen
Keyword(s):  
User Interfaces ◽  
Collaborative Research ◽  
Meta Analysis ◽  
A Priori ◽  
Drug Repurposing ◽  
Data Access ◽  
Estimation Methods ◽  
Large Set ◽  
Computational Tools ◽  
Web Based

: Computational approaches have been proven to be complementary tools of interest in identifying potential candidates for drug repurposing. However, although the methods developed so far offer interesting opportunities and could contribute to solving issues faced by the pharmaceutical sector, they also come with their constraints. Indeed, specific challenges ranging from data access, standardization and integration to the implementation of reliable and coherent validation methods must be addressed to allow systematic use at a larger scale. In this mini-review, we cover computational tools recently developed for addressing some of these challenges. This includes specific databases providing accessibility to a large set of curated data with standardized annotations, web-based tools integrating flexible user interfaces to perform fast computational repurposing experiments and standardized datasets specifically annotated and balanced for validating new computational drug repurposing methods. Interestingly, these new databases combined with the increasing number of information about the outcomes of drug repurposing studies can be used to perform a meta-analysis to identify key properties associated with successful drug repurposing cases. This information could further be used to design estimation methods to compute a priori assessment of the repurposing possibilities.

pLoc_bal-mPlant: Predict Subcellular Localization of Plant Proteins by General PseAAC and Balancing Training Dataset

2019 ◽  
Vol 24 (34) ◽  
pp. 4013-4022 ◽  
Author(s):  
Xiang Cheng ◽  
Xuan Xiao ◽  
Kuo-Chen Chou
Keyword(s):  
Subcellular Localization ◽  
Basic Research ◽  
Training Dataset ◽  
Sequence Information ◽  
Plant Proteins ◽  
Protein Subcellular Localization ◽  
Computational Tools ◽  
Validation Tests ◽  
User Friendly ◽  
Better Than

Knowledge of protein subcellular localization is vitally important for both basic research and drug development. With the avalanche of protein sequences emerging in the post-genomic age, it is highly desired to develop computational tools for timely and effectively identifying their subcellular localization based on the sequence information alone. Recently, a predictor called “pLoc-mPlant” was developed for identifying the subcellular localization of plant proteins. Its performance is overwhelmingly better than that of the other predictors for the same purpose, particularly in dealing with multi-label systems in which some proteins, called “multiplex proteins”, may simultaneously occur in two or more subcellular locations. Although it is indeed a very powerful predictor, more efforts are definitely needed to further improve it. This is because pLoc-mPlant was trained by an extremely skewed dataset in which some subsets (i.e., the protein numbers for some subcellular locations) were more than 10 times larger than the others. Accordingly, it cannot avoid the biased consequence caused by such an uneven training dataset. To overcome such biased consequence, we have developed a new and bias-free predictor called pLoc_bal-mPlant by balancing the training dataset. Cross-validation tests on exactly the same experimentconfirmed dataset have indicated that the proposed new predictor is remarkably superior to pLoc-mPlant, the existing state-of-the-art predictor in identifying the subcellular localization of plant proteins. To maximize the convenience for the majority of experimental scientists, a user-friendly web-server for the new predictor has been established at http://www.jci-bioinfo.cn/pLoc_bal-mPlant/, by which users can easily get their desired results without the need to go through the detailed mathematics.

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