Your browser doesn't support javascript.
loading
Show: 20 | 50 | 100
Results 1 - 3 de 3
Filter
Add more filters










Database
Language
Publication year range
1.
J Mol Graph Model ; 124: 108564, 2023 11.
Article in English | MEDLINE | ID: mdl-37453311

ABSTRACT

PURPOSE OR OBJECTIVE: Melanoma is one of the most dangerous forms of skin cancer and the discovery of novel drugs is an ongoing effort. Quantitative Structure Activity Relationship (QSAR) is a computational method that allows the estimation of the properties of a molecule, including its biological activity. QSAR models have been widely employed in the search for potential drug candidates, but also for agrochemicals and other molecules with applications in different branches of the industry. Here we present Bambu, a simple command line tool to generate QSAR models from high-throughput screening bioassays datasets. METHODS: The tool was developed using the Python programming language and relies mainly on RDKit for molecule data manipulation, FLAML for automated machine learning and the PubChem REST API for data retrieval. As a proof-of-concept we have employed the tool to generate QSAR models for melanoma cell growth inhibition based on HTS data and used them to screen libraries of FDA-approved drugs and natural compounds. Additionally, Bambu was compared to QSAR-Co, another automated tool for QSAR model generation. RESULTS: based on the developed tool we were able to produce QSAR models and identify a wide variety of molecules with potential melanoma cell growth inhibitors, many of which with anti-tumoral activity already described. The QSAR models are available through the URL http://caramel.ufpel.edu.br, and all data and code used to generate its models are available at Zenodo (https://doi.org/10.5281/zenodo.7495214). Bambu source code is available at GitHub (https://github.com/omixlab/bambu-v2). In the benchmark, Bambu was able to produce models with higher accuracy, recall, F1 and ROC AUC when compared to QSAR-Co for the selected datasets. CONCLUSIONS: Bambu is an free and open source tool which facilitates the creation of QSAR models and can be futurely applied in a wide variety of drug discovery projects.


Subject(s)
Drug Discovery , Melanoma , Humans , Drug Discovery/methods , Software , High-Throughput Screening Assays , Machine Learning , Melanoma/drug therapy , Quantitative Structure-Activity Relationship
2.
Int J Biol Macromol ; 193(Pt A): 980-995, 2021 Dec 15.
Article in English | MEDLINE | ID: mdl-34666133

ABSTRACT

Endoglucanases are carbohydrate-degrading enzymes widely used for bioethanol production as part of the enzymatic cocktail. However, family 5 subfamily 5 (GH5_5) endoglucanases are still poorly explored in depth. The Trichoderma reesei representative is the most studied enzyme, presenting catalytic activity in acidic media and mild temperature conditions. Though biochemically similar, its modular structure and synergy with other components vary greatly compared to other GH5_5 members and there is still a lack of specific studies regarding their interaction with other cellulases and application on novel and better mixtures. In this regard, the threedimensional structure elucidation is a highly valuable tool to both uncover basic catalytic mechanisms and implement engineering techniques, proved by the high success rate GH5_5 endoglucanases show. GH5_5 enzymes must be carefully evaluated to fully uncover their potential in biomass-degrading cocktails: the optimal industrial conditions, synergy with other cellulases, structural studies, and enzyme engineering approaches. We aimed to provide the current understanding of these main topics, collecting all available information about characterized GH5_5 endoglucanases function, structure, and bench experiments, in order to suggest future directions to a better application of these enzymes in the industry.


Subject(s)
Cellulase/chemistry , Cellulose/chemistry , Fungal Proteins/chemistry , Trichoderma/enzymology , Hydrolysis
3.
Protein Expr Purif ; 130: 21-27, 2017 02.
Article in English | MEDLINE | ID: mdl-27693624

ABSTRACT

Bovine herpesvirus (BoHV) glycoprotein E (gE) is a non-essential envelope glycoprotein and the deletion of gE has been used to develop BoHV-1 and BoHV-5 differential vaccine strains. The DIVA (Differentiation of Infected from Vaccinated Animals) strategy, using marker vaccines based on gE-negative BoHV strains, allows the identification of vaccinated or infected animals in immunoassays designed to detect anti-gE antibodies. In this study a codon optimized synthetic sequence of gE containing highly conserved regions from BoHV-1 and BoHV-5 was expressed in Pichia pastoris. Following expression, the recombinant gE (rgE) was secreted and purified from the culture medium. The rgE was identified by Western blotting (WB) using sera from cattle naturally infected with BoHV-1 and/or BoHV-5, or sera from bovines experimentally infected with wild-type BoHV-5. Sera collected from cattle vaccinated with a BoHV-5 gI/gE/US9¯ marker vaccine failed to recognise rgE. Expression of rgE, based on a sequence containing highly conserved regions from BoHV-1 and BoHV-5, in P. pastoris enabled the production of large quantities of rgE suitable for use in immunoassays for the differentiation vaccinated or infected cattle.


Subject(s)
Gene Expression , Herpesvirus 1, Bovine/genetics , Herpesvirus 5, Bovine , Infectious Bovine Rhinotracheitis , Pichia/metabolism , Viral Envelope Proteins , Viral Proteins , Animals , Cattle , Herpesvirus 1, Bovine/metabolism , Herpesvirus Vaccines/pharmacology , Infectious Bovine Rhinotracheitis/blood , Infectious Bovine Rhinotracheitis/diagnosis , Pichia/genetics , Recombinant Proteins/chemistry , Recombinant Proteins/genetics , Recombinant Proteins/isolation & purification , Recombinant Proteins/metabolism , Viral Envelope Proteins/chemistry , Viral Envelope Proteins/genetics , Viral Envelope Proteins/isolation & purification , Viral Envelope Proteins/metabolism , Viral Proteins/chemistry , Viral Proteins/genetics , Viral Proteins/isolation & purification , Viral Proteins/metabolism
SELECTION OF CITATIONS
SEARCH DETAIL
...